Well planning system

ABSTRACT

A method can include receiving a digital well plan; issuing drilling instructions for drilling a well based at least in part on the digital well plan; comparing acquired information associated with drilling of the well with well plan information of the digital well plan; and outputting results based at least in part on the comparing of the acquired information with the well plan information.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/646,177, which is a national stage entry of international patentapplication no. PCT/US2018/050314, filed on 11 Sep. 2018, which claimspriority to a U.S. Provisional Application having Ser. No. 62/557,115,filed 11 Sep. 2017. The above applications are incorporated by referenceherein.

BACKGROUND

A bore can be drilled into a geologic environment where the bore may beutilized to form a well. A rig may be a system of components that can beoperated to form a bore in a geologic environment, to transportequipment into and out of a bore in a geologic environment, etc. As anexample, a rig may include a system that can be used to drill a bore andto acquire information about a geologic environment, drilling, etc. Asan example, a rig can include one or more of the following componentsand/or equipment: a mud tank, a mud pump, a derrick or a mast,drawworks, a rotary table or a top drive, a drillstring, powergeneration equipment and auxiliary equipment. As an example, an offshorerig may include one or more of such components, which may be on a vesselor a drilling platform.

SUMMARY

A method can include receiving a digital well plan; issuing drillinginstructions for drilling a well based at least in part on the digitalwell plan; comparing acquired information associated with drilling ofthe well with well plan information of the digital well plan; andoutputting results based at least in part on the comparing of theacquired information with the well plan information. A system caninclude one or more processors; memory operatively coupled to the one ormore processors; and processor-executable instructions stored in thememory and executable by at least one of the one or more processors toinstruct the system to: receive a digital well plan; issue drillinginstructions for drilling a well based at least in part on the digitalwell plan; compare acquired information associated with drilling of thewell with well plan information of the digital well plan; and outputresults based at least in part on the comparing of the acquiredinformation with the well plan information. One or morecomputer-readable storage media can include computer-executableinstructions executable to instruct a computing system to: receive adigital well plan; issue drilling instructions for drilling a well basedat least in part on the digital well plan; compare acquired informationassociated with drilling of the well with well plan information of thedigital well plan; and output results based at least in part on thecomparing of the acquired information with the well plan information.Various other apparatuses, systems, methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be morereadily understood by reference to the following description taken inconjunction with the accompanying drawings.

FIG. 1 illustrates examples of equipment in a geologic environment;

FIG. 2 illustrates an example of a system and examples of types ofholes;

FIG. 3 illustrates an example of a system;

FIG. 4 illustrates an example of a system;

FIG. 5 illustrates an example of a system;

FIG. 6 illustrates an example of a system and an example of a scenario;

FIG. 7 illustrates an example of a wellsite system;

FIG. 8 illustrates an example of a system;

FIG. 9 illustrates an example of a system;

FIG. 10 illustrates an example of a method;

FIG. 11 illustrates an example of a method;

FIG. 12 illustrates an example of a method;

FIG. 13 illustrates an example of a method and an example of a system;

FIG. 14 illustrates an example of a method, an example system and anexample database;

FIG. 15 illustrates examples of GUIs;

FIG. 16 illustrates an example of a GUI;

FIG. 17 illustrates an example of a GUI and examples of notificationgraphical controls;

FIG. 18 illustrates an example of a GUI and examples of notificationgraphical controls;

FIG. 19 illustrates an example of a GUI with an example of a table ofinformation that can be included in a digital well plan;

FIG. 20 illustrates examples of computing and networking equipment; and

FIG. 21 illustrates example components of a system and a networkedsystem.

DETAILED DESCRIPTION

The following description includes embodiments of the best modepresently contemplated for practicing the described implementations.This description is not to be taken in a limiting sense, but rather ismade merely for the purpose of describing the general principles of theimplementations. The scope of the described implementations should beascertained with reference to the issued claims.

Well planning is a process by which a path of a well can be mapped, soas to reach a reservoir, for example, to produce fluids therefrom or,for example, to inject fluids into the reservoir; noting that a fieldmay utilize one or more production wells and one or more injectionwells.

As an example, constraints can be imposed on a design of a well, forexample, a well trajectory may be constrained via one or more physicalphenomena that may impact viability of a bore in earth, ease of drillinginto earth, etc. Thus, for example, one or more constraints may beimposed based at least in part on known geology of a subterranean domainor, for example, presence of one or more other wells in the area (e.g.,collision avoidance). As an example, one or more other constraints maybe imposed, for example, consider one or more constraints germane tocapabilities of tools being used and/or one or more constraints relatedto drilling time and risk tolerance.

As an example, drilling can commence according to a digital well planwhere the drilling progresses along a portion of a trajectory of thedigital well plan. In such an example, based on one or more types ofinformation, a method can include rendering a graphical user interfaceto a display that allows for revising the digital well plan as to one ormore portions of the trajectory that has yet to be drilled.

As an example, a well plan (e.g., a digital well plan) can be generatedbased at least in part on imposed constraints and known information. Asan example, a well plan may be provided to a well owner, approved, andthen implemented by a drilling service provider (e.g., a directionaldriller or “DD”). In such an example, a rig may be used to drill, forexample, according to a well plan. During a period of time during whicha well plan is implemented, a rig may transition from one state toanother state, which may be referred to as rigstates. As an example, astate may be a drilling state or may be a state where drilling into aformation (e.g., rock) is not occurring (e.g., an idle state, atripping-in state, a tripping-out state, etc.).

As an example, a well design system can account for one or morecapabilities of a drilling system or drilling systems that may beutilized at a wellsite. As an example, a drilling engineer may be calledupon to take such capabilities into account, for example, as one or moreof various designs and specifications are created. As an example, astate such as a rigstate may correspond to a capability, for example,while the capability is being utilized.

As an example, a well design system, which may be a well planningsystem, may take into account automation. For example, where a wellsiteincludes wellsite equipment that can be automated, for example, via alocal and/or a remote automation command, a well plan may be generatedin digital form that can be utilized in a well drilling system where atleast some amount of automation is possible and desired. For example, adigital well plan can be accessible by a well drilling system whereinformation in the digital well plan can be utilized via one or moreautomation mechanisms of the well drilling system to automate one ormore operations at a wellsite.

As an example, states such as rigstates may be utilized in planning,implementation, diagnostics, automation, etc. For example, stateinformation may be acquired and stored and/or analyzed. In such anexample, analysis of state information may allow for makingdeterminations as to whether a plan is being adequately followed,equipment is operating as expected, etc.

As an example, a well planning system may utilize a multiple factorapproach where learning occurs based on information for a plurality ofwell that have been drilled, for example, according to one or morecorresponding well plans. Such a system may include machine learning,which may utilize one or more models (e.g., neural network models,etc.). As an example, a trained model or models may be utilized forpurposes of well plan generation, well plan revision and/or one or moreother purposes. As an example, training may occur with respect tooutcomes where an outcome may be classified as being beneficial ordetrimental. In such an example, one or more factors may be associatedwith an outcome and, for example, an outcome itself may be a factor.Where a model is trained based on outcomes (e.g., and underlyingfactors), such a model may be utilized for purposes of well plangeneration, revision, etc., where output may aim to increase likelihoodof one or more positive outcomes and/or decrease likelihood of one ormore negative outcomes. In such a context, positive generally refers tobeneficial while negative generally refers to detrimental; noting that adifference between a planned factor value and actual factor value may bepositive or may be negative, where such difference may have associatedconnotations (e.g., beneficial or detrimental). For example, a positivenumeric difference (e.g., more time than planned) may be a negativeoutcome while a negative numeric difference (e.g., less time thanplanned) may be a beneficial outcome. While time is mentioned, it ispresented as an example as other types of beneficial outcomes anddetrimental outcomes may be identified and utilized for training, wellplan generation, etc.

Various examples of types of environments, various examples of types ofequipment and various examples of types of methods, operations, etc. aredescribed below.

FIG. 1 shows an example of a geologic environment 120. In FIG. 1, thegeologic environment 120 may be a sedimentary basin that includes layers(e.g., stratification) that include a reservoir 121 and that may be, forexample, intersected by a fault 123 (e.g., or faults). As an example,the geologic environment 120 may be outfitted with any of a variety ofsensors, detectors, actuators, etc. For example, equipment 122 mayinclude communication circuitry to receive and/or to transmitinformation with respect to one or more networks 125. Such informationmay include information associated with downhole equipment 124, whichmay be equipment to acquire information, to assist with resourcerecovery, etc. Other equipment 126 may be located remote from a wellsiteand include sensing, detecting, emitting or other circuitry. Suchequipment may include storage and communication circuitry to store andto communicate data, instructions, etc. As an example, one or morepieces of equipment may provide for measurement, collection,communication, storage, analysis, etc. of data (e.g., for one or moreproduced resources, etc.). As an example, one or more satellites may beprovided for purposes of communications, data acquisition, geolocation,etc. For example, FIG. 1 shows a satellite in communication with thenetwork 125 that may be configured for communications, noting that thesatellite may additionally or alternatively include circuitry forimagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 120 as optionally includingequipment 127 and 128 associated with a well that includes asubstantially horizontal portion that may intersect with one or morefractures 129. For example, consider a well in a shale formation thatmay include natural fractures, artificial fractures (e.g., hydraulicfractures) or a combination of natural and artificial fractures. As anexample, a well may be drilled for a reservoir that is laterallyextensive. In such an example, lateral variations in properties,stresses, etc. may exist where an assessment of such variations mayassist with planning, operations, etc. to develop the reservoir (e.g.,via fracturing, injecting, extracting, etc.). As an example, theequipment 127 and/or 128 may include components, a system, systems, etc.for fracturing, seismic sensing, analysis of seismic data, assessment ofone or more fractures, injection, production, etc. As an example, theequipment 127 and/or 128 may provide for measurement, collection,communication, storage, analysis, etc. of data such as, for example,production data (e.g., for one or more produced resources). As anexample, one or more satellites may be provided for purposes ofcommunications, data acquisition, etc.

FIG. 1 also shows an example of equipment 170 and an example ofequipment 180. Such equipment, which may be systems of components, maybe suitable for use in the geologic environment 120. While the equipment170 and 180 are illustrated as land-based, various components may besuitable for use in an offshore system. As shown in FIG. 1, theequipment 180 can be mobile as carried by a vehicle; noting that theequipment 170 can be assembled, disassembled, transported andre-assembled, etc.

The equipment 170 includes a platform 171, a derrick 172, a crown block173, a line 174, a traveling block assembly 175, drawworks 176 and alanding 177 (e.g., a monkeyboard). As an example, the line 174 may becontrolled at least in part via the drawworks 176 such that thetraveling block assembly 175 travels in a vertical direction withrespect to the platform 171. For example, by drawing the line 174 in,the drawworks 176 may cause the line 174 to run through the crown block173 and lift the traveling block assembly 175 skyward away from theplatform 171; whereas, by allowing the line 174 out, the drawworks 176may cause the line 174 to run through the crown block 173 and lower thetraveling block assembly 175 toward the platform 171. Where thetraveling block assembly 175 carries pipe (e.g., casing, etc.), trackingof movement of the traveling block 175 may provide an indication as tohow much pipe has been deployed.

A derrick can be a structure used to support a crown block and atraveling block operatively coupled to the crown block at least in partvia line. A derrick may be pyramidal in shape and offer a suitablestrength-to-weight ratio. A derrick may be movable as a unit or in apiece by piece manner (e.g., to be assembled and disassembled).

As an example, drawworks may include a spool, brakes, a power source andassorted auxiliary devices. Drawworks may controllably reel out and reelin line. Line may be reeled over a crown block and coupled to atraveling block to gain mechanical advantage in a “block and tackle” or“pulley” fashion. Reeling out and in of line can cause a traveling block(e.g., and whatever may be hanging underneath it), to be lowered into orraised out of a bore. Reeling out of line may be powered by gravity andreeling in by a motor, an engine, etc. (e.g., an electric motor, adiesel engine, etc.).

As an example, a crown block can include a set of pulleys (e.g.,sheaves) that can be located at or near a top of a derrick or a mast,over which line is threaded. A traveling block can include a set ofsheaves that can be moved up and down in a derrick or a mast via linethreaded in the set of sheaves of the traveling block and in the set ofsheaves of a crown block. A crown block, a traveling block and a linecan form a pulley system of a derrick or a mast, which may enablehandling of heavy loads (e.g., drillstring, pipe, casing, liners, etc.)to be lifted out of or lowered into a bore. As an example, line may beabout a centimeter to about five centimeters in diameter as, forexample, steel cable. Through use of a set of sheaves, such line maycarry loads heavier than the line could support as a single strand.

As an example, a derrick person may be a rig crew member that works on aplatform attached to a derrick or a mast. A derrick can include alanding on which a derrick person may stand. As an example, such alanding may be about 10 meters or more above a rig floor. In anoperation referred to as trip out of the hole (TOH), a derrick personmay wear a safety harness that enables leaning out from the work landing(e.g., monkeyboard) to reach pipe in located at or near the center of aderrick or a mast and to throw a line around the pipe and pull it backinto its storage location (e.g., fingerboards), for example, until it atime at which it may be desirable to run the pipe back into the bore. Asan example, a rig may include automated pipe-handling equipment suchthat the derrick person controls the machinery rather than physicallyhandling the pipe.

As an example, a trip may refer to the act of pulling equipment from abore and/or placing equipment in a bore. As an example, equipment mayinclude a drillstring that can be pulled out of the hole and/or place orreplaced in the hole. As an example, a pipe trip may be performed wherea drill bit has dulled or has otherwise ceased to drill efficiently andis to be replaced.

FIG. 2 shows an example of a wellsite system 200 (e.g., at a wellsitethat may be onshore or offshore). As shown, the wellsite system 200 caninclude a mud tank 201 for holding mud and other material (e.g., wheremud can be a drilling fluid), a suction line 203 that serves as an inletto a mud pump 204 for pumping mud from the mud tank 201 such that mudflows to a vibrating hose 206, a drawworks 207 for winching drill lineor drill lines 212, a standpipe 208 that receives mud from the vibratinghose 206, a kelly hose 209 that receives mud from the standpipe 208, agooseneck or goosenecks 210, a traveling block 211, a crown block 213for carrying the traveling block 211 via the drill line or drill lines212 (see, e.g., the crown block 173 of FIG. 1), a derrick 214 (see,e.g., the derrick 172 of FIG. 1), a kelly 218 or a top drive 240, akelly drive bushing 219, a rotary table 220, a drill floor 221, a bellnipple 222, one or more blowout preventors (BOPs) 223, a drillstring225, a drill bit 226, a casing head 227 and a flow pipe 228 that carriesmud and other material to, for example, the mud tank 201.

In the example system of FIG. 2, a borehole 232 is formed in subsurfaceformations 230 by rotary drilling; noting that various exampleembodiments may also use directional drilling.

As shown in the example of FIG. 2, the drillstring 225 is suspendedwithin the borehole 232 and has a drillstring assembly 250 that includesthe drill bit 226 at its lower end. As an example, the drillstringassembly 250 may be a bottom hole assembly (BHA).

The wellsite system 200 can provide for operation of the drillstring 225and other operations. As shown, the wellsite system 200 includes theplatform 211 and the derrick 214 positioned over the borehole 232. Asmentioned, the wellsite system 200 can include the rotary table 220where the drillstring 225 pass through an opening in the rotary table220.

As shown in the example of FIG. 2, the wellsite system 200 can includethe kelly 218 and associated components, etc., or a top drive 240 andassociated components. As to a kelly example, the kelly 218 may be asquare or hexagonal metal/alloy bar with a hole drilled therein thatserves as a mud flow path. The kelly 218 can be used to transmit rotarymotion from the rotary table 220 via the kelly drive bushing 219 to thedrillstring 225, while allowing the drillstring 225 to be lowered orraised during rotation. The kelly 218 can pass through the kelly drivebushing 219, which can be driven by the rotary table 220. As an example,the rotary table 220 can include a master bushing that operativelycouples to the kelly drive bushing 219 such that rotation of the rotarytable 220 can turn the kelly drive bushing 219 and hence the kelly 218.The kelly drive bushing 219 can include an inside profile matching anoutside profile (e.g., square, hexagonal, etc.) of the kelly 218;however, with slightly larger dimensions so that the kelly 218 canfreely move up and down inside the kelly drive bushing 219.

As to a top drive example, the top drive 240 can provide functionsperformed by a kelly and a rotary table. The top drive 240 can turn thedrillstring 225. As an example, the top drive 240 can include one ormore motors (e.g., electric and/or hydraulic) connected with appropriategearing to a short section of pipe called a quill, that in turn may bescrewed into a saver sub or the drillstring 225 itself. The top drive240 can be suspended from the traveling block 211, so the rotarymechanism is free to travel up and down the derrick 214. As an example,a top drive 240 may allow for drilling to be performed with more jointstands than a kelly/rotary table approach.

In the example of FIG. 2, the mud tank 201 can hold mud, which can beone or more types of drilling fluids. As an example, a wellbore may bedrilled to produce fluid, inject fluid or both (e.g., hydrocarbons,minerals, water, etc.).

In the example of FIG. 2, the drillstring 225 (e.g., including one ormore downhole tools) may be composed of a series of pipes threadablyconnected together to form a long tube with the drill bit 226 at thelower end thereof. As the drillstring 225 is advanced into a wellborefor drilling, at some point in time prior to or coincident withdrilling, the mud may be pumped by the pump 204 from the mud tank 201(e.g., or other source) via a the lines 206, 208 and 209 to a port ofthe kelly 218 or, for example, to a port of the top drive 240. The mudcan then flow via a passage (e.g., or passages) in the drillstring 225and out of ports located on the drill bit 226 (see, e.g., a directionalarrow). As the mud exits the drillstring 225 via ports in the drill bit226, it can then circulate upwardly through an annular region between anouter surface(s) of the drillstring 225 and surrounding wall(s) (e.g.,open borehole, casing, etc.), as indicated by directional arrows. Insuch a manner, the mud lubricates the drill bit 226 and carries heatenergy (e.g., frictional or other energy) and formation cuttings to thesurface where the mud (e.g., and cuttings) may be returned to the mudtank 201, for example, for recirculation (e.g., with processing toremove cuttings, etc.).

The mud pumped by the pump 204 into the drillstring 225 may, afterexiting the drillstring 225, form a mudcake that lines the wellborewhich, among other functions, may reduce friction between thedrillstring 225 and surrounding wall(s) (e.g., borehole, casing, etc.).A reduction in friction may facilitate advancing or retracting thedrillstring 225. During a drilling operation, the entire drill string225 may be pulled from a wellbore and optionally replaced, for example,with a new or sharpened drill bit, a smaller diameter drill string, etc.As mentioned, the act of pulling a drill string out of a hole orreplacing it in a hole is referred to as tripping. A trip may bereferred to as an upward trip or an outward trip or as a downward tripor an inward trip depending on trip direction.

As an example, consider a downward trip where upon arrival of the drillbit 226 of the drill string 225 at a bottom of a wellbore, pumping ofthe mud commences to lubricate the drill bit 226 for purposes ofdrilling to enlarge the wellbore. As mentioned, the mud can be pumped bythe pump 204 into a passage of the drillstring 225 and, upon filling ofthe passage, the mud may be used as a transmission medium to transmitenergy, for example, energy that may encode information as in mud-pulsetelemetry.

As an example, mud-pulse telemetry equipment may include a downholedevice configured to effect changes in pressure in the mud to create anacoustic wave or waves upon which information may modulated. In such anexample, information from downhole equipment (e.g., one or more modulesof the drillstring 225) may be transmitted uphole to an uphole device,which may relay such information to other equipment for processing,control, etc.

As an example, telemetry equipment may operate via transmission ofenergy via the drillstring 225 itself. For example, consider a signalgenerator that imparts coded energy signals to the drillstring 225 andrepeaters that may receive such energy and repeat it to further transmitthe coded energy signals (e.g., information, etc.).

As an example, the drillstring 225 may be fitted with telemetryequipment 252 that includes a rotatable drive shaft, a turbine impellermechanically coupled to the drive shaft such that the mud can cause theturbine impeller to rotate, a modulator rotor mechanically coupled tothe drive shaft such that rotation of the turbine impeller causes saidmodulator rotor to rotate, a modulator stator mounted adjacent to orproximate to the modulator rotor such that rotation of the modulatorrotor relative to the modulator stator creates pressure pulses in themud, and a controllable brake for selectively braking rotation of themodulator rotor to modulate pressure pulses. In such example, analternator may be coupled to the aforementioned drive shaft where thealternator includes at least one stator winding electrically coupled toa control circuit to selectively short the at least one stator windingto electromagnetically brake the alternator and thereby selectivelybrake rotation of the modulator rotor to modulate the pressure pulses inthe mud.

In the example of FIG. 2, an uphole control and/or data acquisitionsystem 262 may include circuitry to sense pressure pulses generated bytelemetry equipment 252 and, for example, communicate sensed pressurepulses or information derived therefrom for process, control, etc.

The assembly 250 of the illustrated example includes alogging-while-drilling (LWD) module 254, a measuring-while-drilling(MWD) module 256, an optional module 258, a roto-steerable system andmotor 260, and the drill bit 226.

The LWD module 254 may be housed in a suitable type of drill collar andcan contain one or a plurality of selected types of logging tools. Itwill also be understood that more than one LWD and/or MWD module can beemployed, for example, as represented at by the module 256 of thedrillstring assembly 250. Where the position of an LWD module ismentioned, as an example, it may refer to a module at the position ofthe LWD module 254, the module 256, etc. An LWD module can includecapabilities for measuring, processing, and storing information, as wellas for communicating with the surface equipment. In the illustratedexample, the LWD module 254 may include a seismic measuring device.

The MWD module 256 may be housed in a suitable type of drill collar andcan contain one or more devices for measuring characteristics of thedrillstring 225 and the drill bit 226. As an example, the MWD tool 254may include equipment for generating electrical power, for example, topower various components of the drillstring 225. As an example, the MWDtool 254 may include the telemetry equipment 252, for example, where theturbine impeller can generate power by flow of the mud; it beingunderstood that other power and/or battery systems may be employed forpurposes of powering various components. As an example, the MWD module256 may include one or more of the following types of measuring devices:a weight-on-bit measuring device, a torque measuring device, a vibrationmeasuring device, a shock measuring device, a stick slip measuringdevice, a direction measuring device, and an inclination measuringdevice.

FIG. 2 also shows some examples of types of holes that may be drilled.For example, consider a slant hole 272, an S-shaped hole 274, a deepinclined hole 276 and a horizontal hole 278.

As an example, a drilling operation can include directional drillingwhere, for example, at least a portion of a well includes a curved axis.For example, consider a radius that defines curvature where aninclination with regard to the vertical may vary until reaching an anglebetween about 30 degrees and about 60 degrees or, for example, an angleto about 90 degrees or possibly greater than about 90 degrees.

As an example, a directional well can include several shapes where eachof the shapes may aim to meet particular operational demands. As anexample, a drilling process may be performed on the basis of informationas and when it is relayed to a drilling engineer. As an example,inclination and/or direction may be modified based on informationreceived during a drilling process.

As an example, deviation of a bore may be accomplished in part by use ofa downhole motor and/or a turbine. As to a motor, for example, adrillstring can include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipmentto perform method such as geosteering. As an example, a steerable systemcan include a PDM or of a turbine on a lower part of a drillstringwhich, just above a drill bit, a bent sub can be mounted. As an example,above a PDM, MWD equipment that provides real time or near real timedata of interest (e.g., inclination, direction, pressure, temperature,real weight on the drill bit, torque stress, etc.) and/or LWD equipmentmay be installed. As to the latter, LWD equipment can make it possibleto send to the surface various types of data of interest, including forexample, geological data (e.g., gamma ray log, resistivity, density andsonic logs, etc.).

The coupling of sensors providing information on the course of a welltrajectory, in real time or near real time, with, for example, one ormore logs characterizing the formations from a geological viewpoint, canallow for implementing a geosteering method. Such a method can includenavigating a subsurface environment, for example, to follow a desiredroute to reach a desired target or targets.

As an example, a drillstring can include an azimuthal density neutron(ADN) tool for measuring density and porosity; a MWD tool for measuringinclination, azimuth and shocks; a compensated dual resistivity (CDR)tool for measuring resistivity and gamma ray related phenomena; one ormore variable gauge stabilizers; one or more bend joints; and ageosteering tool, which may include a motor and optionally equipment formeasuring and/or responding to one or more of inclination, resistivityand gamma ray related phenomena.

As an example, geosteering can include intentional directional controlof a wellbore based on results of downhole geological loggingmeasurements in a manner that aims to keep a directional wellbore withina desired region, zone (e.g., a pay zone), etc. As an example,geosteering may include directing a wellbore to keep the wellbore in aparticular section of a reservoir, for example, to minimize gas and/orwater breakthrough and, for example, to maximize economic productionfrom a well that includes the wellbore.

Referring again to FIG. 2, the wellsite system 200 can include one ormore sensors 264 that are operatively coupled to the control and/or dataacquisition system 262. As an example, a sensor or sensors may be atsurface locations. As an example, a sensor or sensors may be at downholelocations. As an example, a sensor or sensors may be at one or moreremote locations that are not within a distance of the order of aboutone hundred meters from the wellsite system 200. As an example, a sensoror sensor may be at an offset wellsite where the wellsite system 200 andthe offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 264 can be provided fortracking pipe, tracking movement of at least a portion of a drillstring,etc.

As an example, the system 200 can include one or more sensors 266 thatcan sense and/or transmit signals to a fluid conduit such as a drillingfluid conduit (e.g., a drilling mud conduit). For example, in the system200, the one or more sensors 266 can be operatively coupled to portionsof the standpipe 208 through which mud flows. As an example, a downholetool can generate pulses that can travel through the mud and be sensedby one or more of the one or more sensors 266. In such an example, thedownhole tool can include associated circuitry such as, for example,encoding circuitry that can encode signals, for example, to reducedemands as to transmission. As an example, circuitry at the surface mayinclude decoding circuitry to decode encoded information transmitted atleast in part via mud-pulse telemetry. As an example, circuitry at thesurface may include encoder circuitry and/or decoder circuitry andcircuitry downhole may include encoder circuitry and/or decodercircuitry. As an example, the system 200 can include a transmitter thatcan generate signals that can be transmitted downhole via mud (e.g.,drilling fluid) as a transmission medium.

As an example, one or more portions of a drillstring may become stuck.The term stuck can refer to one or more of varying degrees of inabilityto move or remove a drillstring from a bore. As an example, in a stuckcondition, it might be possible to rotate pipe or lower it back into abore or, for example, in a stuck condition, there may be an inability tomove the drillstring axially in the bore, though some amount of rotationmay be possible. As an example, in a stuck condition, there may be aninability to move at least a portion of the drillstring axially androtationally.

As to the term “stuck pipe”, the can refer to a portion of a drillstringthat cannot be rotated or moved axially. As an example, a conditionreferred to as “differential sticking” can be a condition whereby thedrillstring cannot be moved (e.g., rotated or reciprocated) along theaxis of the bore. Differential sticking may occur when high-contactforces caused by low reservoir pressures, high wellbore pressures, orboth, are exerted over a sufficiently large area of the drillstring.Differential sticking can have time and financial cost.

As an example, a sticking force can be a product of the differentialpressure between the wellbore and the reservoir and the area that thedifferential pressure is acting upon. This means that a relatively lowdifferential pressure (delta p) applied over a large working area can bejust as effective in sticking pipe as can a high differential pressureapplied over a small area.

As an example, a condition referred to as “mechanical sticking” can be acondition where limiting or prevention of motion of the drillstring by amechanism other than differential pressure sticking occurs. Mechanicalsticking can be caused, for example, by one or more of junk in the hole,wellbore geometry anomalies, cement, keyseats or a buildup of cuttingsin the annulus.

FIG. 3 shows an example of a system 300 that includes various equipmentfor evaluation 310, planning 320, engineering 330 and operations 340.For example, a drilling workflow framework 301, a seismic-to-simulationframework 302, a technical data framework 303 and a drilling framework304 may be implemented to perform one or more processes such as aevaluating a formation 314, evaluating a process 318, generating atrajectory 324, validating a trajectory 328, formulating constraints334, designing equipment and/or processes based at least in part onconstraints 338, performing drilling 344 and evaluating drilling and/orformation 348.

In the example of FIG. 3, the seismic-to-simulation framework 302 canbe, for example, the PETREL® framework (Schlumberger Limited, Houston,Tex.) and the technical data framework 303 can be, for example, theTECHLOG® framework (Schlumberger Limited, Houston, Tex.).

As an example, a framework can include entities that may include earthentities, geological objects or other objects such as wells, surfaces,reservoirs, etc. Entities can include virtual representations of actualphysical entities that are reconstructed for purposes of one or more ofevaluation, planning, engineering, operations, etc.

Entities may include entities based on data acquired via sensing,observation, etc. (e.g., seismic data and/or other information). Anentity may be characterized by one or more properties (e.g., ageometrical pillar grid entity of an earth model may be characterized bya porosity property). Such properties may represent one or moremeasurements (e.g., acquired data), calculations, etc.

A framework may be an object-based framework. In such a framework,entities may include entities based on pre-defined classes, for example,to facilitate modeling, analysis, simulation, etc. An example of anobject-based framework is the MICROSOFT™ .NET™ framework (Redmond,Wash.), which provides a set of extensible object classes. In the .NET™framework, an object class encapsulates a module of reusable code andassociated data structures. Object classes can be used to instantiateobject instances for use in by a program, script, etc. For example,borehole classes may define objects for representing boreholes based onwell data.

As an example, a framework can include an analysis component that mayallow for interaction with a model or model-based results (e.g.,simulation results, etc.). As to simulation, a framework may operativelylink to or include a simulator such as the ECLIPSE® reservoir simulator(Schlumberger Limited, Houston Tex.), the INTERSECT® reservoir simulator(Schlumberger Limited, Houston Tex.), etc.

The aforementioned PETREL® framework provides components that allow foroptimization of exploration and development operations. The PETREL®framework includes seismic to simulation software components that canoutput information for use in increasing reservoir performance, forexample, by improving asset team productivity. Through use of such aframework, various professionals (e.g., geophysicists, geologists, wellengineers, reservoir engineers, etc.) can develop collaborativeworkflows and integrate operations to streamline processes. Such aframework may be considered an application and may be considered adata-driven application (e.g., where data is input for purposes ofmodeling, simulating, etc.).

The PETREL® framework can include components for implementation of acognitive environment, which can include machine learning, training ofmachines and use of trained machines. As an example, the PETREL®framework can include one or more cloud-based components, for example,for purposes of computations, access, data storage, transmission ofinstructions to equipment, etc. A cloud framework may be included in asystem that can provision and utilize resources (e.g., AZURE™ cloudframework (Microsoft Corp., Redmond, Wash.), etc.).

The PETREL® framework can be implemented to unite planning andoperations, for example, consider one or more planning tasks performedvia the framework and one or more planned tasks being executed duringfield operations. As an example, a framework can include issuing one ormore instructions via one or more networks to one or more pieces offield equipment to cause such equipment to perform one or moreoperations (e.g., data acquisition, sensing, moving, fracturing,drilling, tripping, casing, lifting via gas lift equipment, lifting viapump equipment, etc.).

As an example, a framework can include components to automate andaccelerate complex functions such as modeling, simulation, analysis, andforecasting. Such advanced computational capabilities can be based oninsights from lab and/or field measurements, datasets across a range ofdiverse sources.

As an example, one or more frameworks may be interoperative and/or runupon one or another. As an example, consider the OCEAN® frameworkenvironment (Schlumberger Limited, Houston, Tex.), which allows forintegration of add-ons (or plug-ins) into a PETREL® framework workflow.The OCEAN® framework environment leverages various tools (e.g., hardwareand software) and offers stable, user-friendly interfaces for efficientdevelopment. In an example embodiment, various components may beimplemented as add-ons (or plug-ins) that conform to and operateaccording to specifications of a framework environment (e.g., accordingto application programming interface (API) specifications, etc.).

As an example, a framework can include a model simulation layer alongwith a framework services layer, a framework core layer and a moduleslayer. In an example embodiment, an application may be considered adata-driven application. As an example, a framework can includecomponents for model building and visualization. Such a model mayinclude one or more grids. For example, a geologic region can berepresented as a numerical model that includes one or more spatial gridsthat represent features, physics, etc., of the geologic region. Such anumerical model can be utilized for one or more purposes such as, forexample, visualization, data acquisition, simulation, control, etc.

As an example, the model simulation layer may provide domain objects,act as a data source, provide for rendering and provide for various userinterfaces. Rendering may provide a graphical environment in whichapplications can display their data while the user interfaces mayprovide a common look and feel for application user interfacecomponents. A user interface can be a graphical user interface (GUI)that is rendered to a display of a computing device or system. A GUI canbe rendered based in part on execution of instructions by a processor orprocessors. As an example, a digital well plan can include instructionsthat can be utilized to render a GUI or GUIs. In such an example, theGUI or GUIs can include features for selection of information and/or oneor more graphical controls (e.g., via a mouse, a stylus, a finger, voicecommand, etc.) where such selection can cause a computing device orsystem, locally and/or remotely, to perform one or more actions. As anexample, one or more pieces of field equipment may be operativelycoupled to one or more computing devices or systems, for example, viaone or more interfaces (e.g., network interfaces, etc.). In such anexample, one or more instructions may be issued to one or more pieces ofequipment responsive to interactions with a GUI or GUIs that operatebased at least in part on information in a digital well plan.

As an example, with respect to a model that is object-based, domainobjects can include entity objects, property objects and optionallyother objects. Entity objects may be used to geometrically representwells or other equipment, surfaces, reservoirs, etc., while propertyobjects may be used to provide property values as well as data versionsand display parameters. For example, an entity object may represent awell where a property object provides log information as well as versioninformation and display information (e.g., to display the well as partof a model). As an example, an entity object may represent a piece ofequipment at a rigsite or a wellsite where information stored in adigital well plan may be utilized to control the piece of equipment. Forexample, consider a GUI that renders a graphical representation of thepiece of equipment at a rigsite or a wellsite via an entity object to adisplay where a user can interact with the entity object and hence thepiece of equipment at the rigsite or the wellsite. In such an example,the interaction may cause information to be transmitted to the piece ofequipment where the information may include information of a digitalwell plan (e.g., a control command, a parameter value, etc.).

As an example, data may be stored in one or more data sources (or datastores, generally physical data storage devices), which may be at thesame or different physical sites and accessible via one or morenetworks. As an example, a model simulation layer may be configured tomodel projects. As such, a particular project may be stored where storedproject information may include inputs, models, results and cases. Thus,upon completion of a modeling session, a user may store a project. At alater time, the project can be accessed and restored using the modelsimulation layer, which can recreate instances of the relevant domainobjects.

As an example, the system 300 may be used to perform one or moreworkflows. A workflow may be a process that includes a number ofworksteps. A workstep may operate on data, for example, to create newdata, to update existing data, etc. As an example, a workflow mayoperate on one or more inputs and create one or more results, forexample, based on one or more algorithms. As an example, a system mayinclude a workflow editor for creation, editing, executing, etc. of aworkflow. In such an example, the workflow editor may provide forselection of one or more pre-defined worksteps, one or more customizedworksteps, etc. As an example, a workflow may be a workflowimplementable at least in part via a framework, for example, consider aframework that operates directly and/or indirectly on seismic data,seismic attribute(s), log data, etc.

As an example, seismic data can be data acquired via a seismic surveywhere sources and receivers are positioned in a geologic environment toemit and receive seismic energy where at least a portion of such energycan reflect off subsurface structures. Seismic data can be referred toas seismic image data as it can be utilized to generate an image of asubsurface region.

Seismic reflection technology and techniques to image subsurfacestructure can involve performing one or more surveys where, for example,geometry of one or more sources and one or more receivers tend to beknown (e.g., according to an acquisition geometry). As an example,seismic imaging can include processing of seismic image data utilizingone or more processors via a process akin to triangulation, though morecomplex, which aims to place reflections in their proper locations with(e.g., more-or-less) proper amplitudes, which can then be interpreted.In reflection seismology, the amplitudes tend to be indicative ofrelative changes in impedance, and, for a 3D spatial survey, a seismicvolume (e.g., volumetric seismic image data) can be processed to yieldimpedances of subsurface material between reflecting boundaries.

As an example, a seismic data analysis framework or frameworks (e.g.,consider the OMEGA® framework, Schlumberger Limited, Houston, Tex.) maybe utilized to determine depth, extent, properties, etc. of subsurfacestructures. As an example, seismic data analysis can include forwardmodeling and/or inversion, for example, to iteratively build a model ofa subsurface region of a geologic environment. As an example, a seismicdata analysis framework may be part of or operatively coupled to aseismic-to-simulation framework (e.g., the PETREL® framework, etc.).

As an example, a velocity model of a geologic region can includeacoustic and/or elastic information (e.g., acoustic impedance and/orelastic impedance). Acoustic impedance is the product of density andseismic velocity, which varies among different rock layers, commonlysymbolized by Z. The difference in acoustic impedance between rocklayers affects the reflection coefficient. Elastic impedance is theproduct of the density of a medium and its shear wave velocity. As X-raycomputed tomography depends on the X-ray attenuation coefficients as amaterial property of material imaged to generate pixel values that canbe rendered as an image to a display, reflection seismology depends onmaterial properties of material imaged to general pixel values that canbe rendered as an image to a display. In other words, a rendered seismicimage is an image that depends on properties of material in a geologicregion that is imaged. Structural features such as beds, layers,geobodies, faults, etc., can be discerned through reflection seismologywhere such features can be utilized for model building, modelrefinement, planning, generation of a digital well plan, execution of adigital well plan, production of hydrocarbons from a reservoir, etc. Asan example, seismic image data from reflection seismology of a geologicregion can be inverted to generate a model or a refined model of thegeologic region.

As an example, a framework may provide for modeling petroleum systems.For example, the PETROMOD® framework (Schlumberger Limited, Houston,Tex.) includes features for input of various types of information (e.g.,seismic, well, geological, etc.) to model evolution of a sedimentarybasin. The PETROMOD® framework provides for petroleum systems modelingvia input of various data such as seismic data, well data and othergeological data, for example, to model evolution of a sedimentary basin.The PETROMOD® framework may predict if, and how, a reservoir has beencharged with hydrocarbons, including, for example, the source and timingof hydrocarbon generation, migration routes, quantities, pore pressureand hydrocarbon type in the subsurface or at surface conditions. Incombination with a framework such as the PETREL® framework, workflowsmay be constructed to provide basin-to-prospect scale explorationsolutions. Data exchange between frameworks can facilitate constructionof models, analysis of data (e.g., PETROMOD® framework data analyzedusing PETREL® framework capabilities), and coupling of workflows.

As mentioned, a drillstring can include various tools that may makemeasurements. As an example, a wireline tool or another type of tool maybe utilized to make measurements. As an example, a tool may beconfigured to acquire electrical borehole images. As an example, thefullbore Formation MicroImager (FMI) tool (Schlumberger Limited,Houston, Tex.) can acquire borehole image data. A data acquisitionsequence for such a tool can include running the tool into a boreholewith acquisition pads closed, opening and pressing the pads against awall of the borehole, delivering electrical current into the materialdefining the borehole while translating the tool in the borehole, andsensing current remotely, which is altered by interactions with thematerial.

Analysis of formation information may reveal features such as, forexample, vugs, dissolution planes (e.g., dissolution along beddingplanes), stress-related features, dip events, etc. As an example, a toolmay acquire information that may help to characterize a reservoir,optionally a fractured reservoir where fractures may be natural and/orartificial (e.g., hydraulic fractures). As an example, informationacquired by a tool or tools may be analyzed using a framework such asthe TECHLOG® framework. As an example, the TECHLOG® framework can beinteroperable with one or more other frameworks such as, for example,the PETREL® framework.

FIG. 4 shows an example of a system 400 that includes a client layer410, an applications layer 440 and a storage layer 460. As shown theclient layer 410 can be in communication with the applications layer 440and the applications layer 440 can be in communication with the storagelayer 460.

The client layer 410 can include features that allow for access andinteractions via one or more private networks 412, one or more mobileplatforms and/or mobile networks 414 and via the “cloud” 416, which maybe considered to include distributed equipment that forms a network suchas a network of networks.

In the example of FIG. 4, the applications layer 440 includes thedrilling workflow framework 301 as mentioned with respect to the exampleof FIG. 3. The applications layer 440 also includes a databasemanagement component 442 that includes one or more search enginesmodules.

As an example, the database management component 442 can include one ormore search engine modules that provide for searching one or moreinformation that may be stored in one or more data repositories. As anexample, the STUDIO E&P™ knowledge environment (Schlumberger Ltd.,Houston, Tex.) includes STUDIO FIND™ search functionality, whichprovides a search engine. In the oil and gas industry, “E&P” is anacronym for exploration and production, which include performing variousactions in the field. The STUDIO FIND™ search functionality can providefor indexing content, for example, to create one or more indexes. As anexample, search functionality may provide for access to public content,private content or both, which may exist in one or more databases, forexample, optionally distributed and accessible via an intranet, theInternet or one or more other networks. As an example, a search enginemay be configured to apply one or more filters from a set or sets offilters, for example, to enable users to filter out data that may not beof interest.

As an example, a framework may provide for interaction with a searchengine and, for example, associated features such as features of theSTUDIO FIND™ search functionality. As an example, a framework mayprovide for implementation of one or more spatial filters (e.g., basedon an area viewed on a display, static data, etc.). As an example, asearch may provide access to dynamic data (e.g., “live” data from one ormore sources), which may be available via one or more networks (e.g.,wired, wireless, etc.). As an example, one or more modules mayoptionally be implemented within a framework or, for example, in amanner operatively coupled to a framework (e.g., as an add-on, aplug-in, etc.). As an example, a module for structuring search results(e.g., in a list, a hierarchical tree structure, etc.) may optionally beimplemented within a framework or, for example, in a manner operativelycoupled to a framework (e.g., as an add-on, a plug-in, etc.).

In the example of FIG. 4, the applications layer 440 can includecommunicating with one or more resources such as, for example, theseismic-to-simulation framework 302, the drilling framework 304 and/orone or more sites, which may be or include one or more offset wellsites.As an example, the applications layer 440 may be implemented for aparticular wellsite where information can be processed as part of aworkflow for operations such as, for example, operations performed,being performed and/or to be performed at the particular wellsite. As anexample, an operation may involve directional drilling, for example, viageosteering. Geosteering can include intentional directional control ofa well based on the results of downhole geological logging measurements,which may be used alternatively or additionally to multi-dimensionaltargets in space. As an example, geosteering may be used to keep aborehole in a particular section of a reservoir to minimize gas or waterbreakthrough and maximize economic production from a completed well.

In the example of FIG. 4, the storage layer 460 can include varioustypes of data, information, etc., which may be stored in one or moredatabases 462. As an example, one or more servers 464 may provide formanagement, access, etc., to data, information, etc., stored in the oneor more databases 462. As an example, the module 442 may provide forsearching as to data, information, etc., stored in the one or moredatabases 462.

As an example, the module 442 may include features for indexing, etc. Asan example, information may be indexed at least in part with respect towellsite. For example, where the applications layer 440 is implementedto perform one or more workflows associated with a particular wellsite,data, information, etc., associated with that particular wellsite may beindexed based at least in part on the wellsite being an index parameter(e.g., a search parameter).

As an example, the system 400 of FIG. 4 may be implemented to performone or more portions of one or more workflows associated with the system300 of FIG. 3. For example, the drilling workflow framework 301 mayinteract with the technical data framework 303 and the drillingframework 304 before, during and/or after performance of one or moredrilling operations. In such an example, the one or more drillingoperations may be performed in a geologic environment (see, e.g., theenvironment 150 of FIG. 1) using one or more types of equipment (see,e.g., equipment of FIGS. 1 and 2).

FIG. 5 shows an example of a system 500 that includes a computing device501, an application services block 510, a bootstrap services block 520,a cloud gateway block 530, a cloud portal block 540, a stream processingservices block 550, one or more databases 560, a management servicesblock 570 and a service systems manager 590.

In the example of FIG. 5, the computing device 501 can include one ormore processors 502, memory 503, one or more interfaces 504 and locationcircuitry 505 or, for example, one of the one or more interfaces 504 maybe operatively coupled to location circuitry that can acquire locallocation information. For example, the computing device 501 can includeGPS circuitry as location circuitry such that the approximate locationof the computer device 501 can be determined. While GPS is mentioned(Global Positioning System), location circuitry may employ one or moretypes of locating techniques. For example, consider one or more ofGLONASS, GALILEO, BeiDou-2, or another system (e.g., global navigationsatellite system, “GNSS”). As an example, location circuitry may includecellular phone circuitry (e.g., LTE, 3G, 4G, etc.). As an example,location circuitry may include Wi-Fi circuitry.

As an example, the application services block 510 can be implemented viainstructions executable using the computing device 501. As an example,the computing device 501 may be at a wellsite and part of wellsiteequipment. As an example, the computing device 501 may be a mobilecomputing device (e.g., tablet, laptop, etc.) or a desktop computingdevice that may be mobile, for example, as part of wellsite equipment(e.g., doghouse equipment, rig equipment, vehicle equipment, etc.).

As an example, the system 500 can include performing various actions.For example, the system 500 may include a token that is utilized as asecurity measure to assure that information (e.g., data) is associatedwith appropriate permission or permissions for transmission, storage,access, etc.

In the example of FIG. 5, various circles are shown with labels A to H.As an example, A can be a process where an administrator creates ashared access policy (e.g., manually, via an API, etc.); B can be aprocess for allocating a shared access key for a device identifier(e.g., a device ID), which may be performed manually, via an API, etc.);C can be a process for creating a “device” that can be registered in adevice registry and for allocating a symmetric key; D can be a processfor persisting metadata where such metadata may be associated with awellsite identifier (e.g., a well ID) and where, for example, locationinformation (e.g., GPS based information, etc.) may be associated with adevice ID and a well ID; E can be a process where a bootstrap messagepasses that includes a device ID (e.g., a trusted platform module (TPM)chip ID that may be embedded within a device) and that includes a wellID and location information such that bootstrap services (e.g., of thebootstrap services block 520) can proceed to obtain shared accesssignature (SAS) key(s) to a cloud service endpoint for authorization; Fcan be a process for provisioning a device, for example, if not alreadyprovisioned, where, for example, the process can include returningdevice keys and endpoint; G can be a process for getting a SAS tokenusing an identifier and a key; and H can be a process that includesbeing ready to send a message using device credentials. Also shown inFIG. 5 is a process for getting a token and issuing a command for a wellidentifier (see label Z).

As an example, Shared Access Signatures can be an authenticationmechanism based on, for example, SHA-256 secure hashes, URIs, etc. As anexample, SAS may be used by one or more Service Bus services. SAS can beimplemented via a Shared Access Policy and a Shared Access Signature,which may be referred to as a token. As an example, for SAS applicationsusing the AZURE™ .NET SDK with the Service Bus, .NET libraries can useSAS authorization through the SharedAccessSignatureToken Provider class.

As an example, where a system gives an entity (e.g., a sender, a client,etc.) a SAS token, that entity does not have the key directly, and thatentity cannot reverse the hash to obtain it. As such, there is controlover what that entity can access and, for example, for how long accessmay exist. As an example, in SAS, for a change of the primary key in thepolicy, Shared Access Signatures created from it will be invalidated.

As an example, the system 500 of FIG. 5 can be implemented forprovisioning of rig acquisition system and/or data delivery.

As an example, a method can include establishing an Internet of Things(IoT) hub or hubs. As an example, such a hub or hubs can include one ormore device registries. In such an example, the hub or hubs may providefor storage of metadata associated with a device and, for example, aper-device authentication model. As an example, where locationinformation indicates that a device (e.g., wellsite equipment, etc.) hasbeen changed with respect to its location, a method can include revokingthe device in a hub.

As an example, an architecture utilized in a system such as, forexample, the system 500 may include features of the AZURE™ architecture.As an example, the cloud portal block 540 can include one or morefeatures of an AZURE™ portal that can manage, mediate, etc., access toone or more services, data, connections, networks, devices, etc.

As an example, the system 500 can include a cloud computing platform andinfrastructure, for example, for building, deploying, and managingapplications and services (e.g., through a network of datacenters,etc.). As an example, such a cloud platform may provide PaaS and IaaSservices and support one or more different programming languages, toolsand frameworks, etc.

FIG. 6 shows an example of a system 600 associated with an example of awellsite system 601 and also shows an example scenario 602. As shown inFIG. 6, the system 600 can include a front-end 603 and a back-end 605from an outside or external perspective (e.g., external to the wellsitesystem 601, etc.). In the example of FIG. 6, the system 600 includes adrilling framework 620, a stream processing and/or management block 640,storage 660 and optionally one or more other features 680 that can bedefined as being back-end features. In the example of FIG. 6, the system600 includes a drilling workflow framework 610, a stream processingand/or management block 630, applications 650 and optionally one or moreother features 670 that can be defined as being front-end features.

As an example, a user operating a user device can interact with thefront-end 603 where the front-end 603 can interact with one or morefeatures of the back-end 605. As an example, such interactions may beimplemented via one or more networks, which may be associated with acloud platform (e.g., cloud resources, etc.).

As to the example scenario 602, the drilling framework 620 can provideinformation associated with, for example, the wellsite system 601. Asshown, the stream blocks 630 and 640, a query service 685 and thedrilling workflow framework 610 may receive information and direct suchinformation to storage, which may include a time series database 662, ablob storage database 664, a document database 666, a well informationdatabase 668, a project(s) database 669, etc. As an example, the wellinformation database 668 may receive and store information such as, forexample, customer information (e.g., from entities that may be owners ofrights at a wellsite, service providers at a wellsite, etc.). As anexample, the project database 669 can include information from aplurality of projects where a project may be, for example, a wellsiteproject.

As an example, the system 600 can be operable for a plurality ofwellsites, which may include active and/or inactive wellsites and/or,for example, one or more planned wellsites. As an example, the system600 can include various components of the system 300 of FIG. 3. As anexample, the system 600 can include various components of the system 400of FIG. 4. For example, the drilling workflow framework 610 can be adrilling workflow framework such as the drilling workflow framework 301and/or, for example, the drilling framework 620 can be a drillingframework such as the drilling framework 304.

FIG. 7 shows an example of a wellsite system 700, specifically, FIG. 7shows the wellsite system 700 in an approximate side view and anapproximate plan view along with a block diagram of a system 770.

In the example of FIG. 7, the wellsite system 700 can include a cabin710, a rotary table 722, drawworks 724, a mast 726 (e.g., optionallycarrying a top drive, etc.), mud tanks 730 (e.g., with one or morepumps, one or more shakers, etc.), one or more pump buildings 740, aboiler building 742, a hydraulic power unit (HPU) building 744 (e.g.,with a rig fuel tank, etc.), a combination building 748 (e.g., with oneor more generators, etc.), pipe tubs 762, a catwalk 764, a flare 768,etc. Such equipment can include one or more associated functions and/orone or more associated operational risks, which may be risks as to time,resources, and/or humans.

As shown in the example of FIG. 7, the wellsite system 700 can include asystem 770 that includes one or more processors 772, memory 774operatively coupled to at least one of the one or more processors 772,instructions 776 that can be, for example, stored in the memory 774, andone or more interfaces 778. As an example, the system 770 can includeone or more processor-readable media that include processor-executableinstructions executable by at least one of the one or more processors772 to cause the system 770 to control one or more aspects of thewellsite system 700. In such an example, the memory 774 can be orinclude the one or more processor-readable media where theprocessor-executable instructions can be or include instructions. As anexample, a processor-readable medium can be a computer-readable storagemedium that is not a signal and that is not a carrier wave.

FIG. 7 also shows a battery 780 that may be operatively coupled to thesystem 770, for example, to power the system 770. As an example, thebattery 780 may be a back-up battery that operates when another powersupply is unavailable for powering the system 770. As an example, thebattery 780 may be operatively coupled to a network, which may be acloud network. As an example, the battery 780 can include smart batterycircuitry and may be operatively coupled to one or more pieces ofequipment via a SMBus or other type of bus.

In the example of FIG. 7, services 790 are shown as being available, forexample, via a cloud platform. Such services can include data services792, query services 794 and drilling services 796. As an example, theservices 790 may be part of a system such as the system 300 of FIG. 3,the system 400 of FIG. 4 and/or the system 600 of FIG. 6.

As an example, a system such as, for example, the system 300 of FIG. 3may be utilized to perform a workflow. Such a system may be distributedand allow for collaborative workflow interactions and may be consideredto be a platform (e.g., a framework for collaborative interactions,etc.).

As an example, one or more systems can be utilized to implement aworkflow that can be performed collaboratively. As an example, thesystem 300 of FIG. 3 can be operated as a distributed, collaborativewell-planning system. The system 300 can utilize one or more servers,one or more client devices, etc. and may maintain one or more databases,data files, etc., which may be accessed and modified by one or moreclient devices, for example, using a web browser, remote terminal, etc.As an example, a client device may modify a database or data fileson-the-fly, and/or may include “sandboxes” that may permit one or moreclient devices to modify at least a portion of a database or data filesoptionally off-line, for example, without affecting a database or datafiles seen by one or more other client devices. As an example, a clientdevice that includes a sandbox may modify a database or data file aftercompleting an activity in the sandbox.

In some examples, client devices and/or servers may be remote withrespect to one another and/or may individually include two or moreremote processing units. As an example, two systems can be “remote” withrespect to one another if they are not physically proximate to oneanother; for example, two devices that are located at different sides ofa room, in different rooms, in different buildings, in different cities,countries, etc. may be considered “remote” depending on the context. Insome embodiments, two or more client devices may be proximate to oneanother, and/or one or more client devices and a server may be proximateto one another.

As an example, various aspects of a workflow may be completedautomatically, may be partially automated, or may be completed manually,as by a human user interfacing with a software application. As anexample, a workflow may be cyclic, and may include, as an example, fourstages such as, for example, an evaluation stage (see, e.g., theevaluation equipment 310), a planning stage (see, e.g., the planningequipment 320), an engineering stage (see, e.g., the engineeringequipment 330) and an execution stage (see, e.g., the operationsequipment 340). As an example, a workflow may commence at one or morestages, which may progress to one or more other stages (e.g., in aserial manner, in a parallel manner, in a cyclical manner, etc.).

As an example, a workflow can commence with an evaluation stage, whichmay include a geological service provider evaluating a formation (see,e.g., the evaluation block 314). As an example, a geological serviceprovider may undertake the formation evaluation using a computing systemexecuting a software package tailored to such activity; or, for example,one or more other suitable geology platforms may be employed (e.g.,alternatively or additionally). As an example, the geological serviceprovider may evaluate the formation, for example, using earth models,geophysical models, basin models, petrotechnical models, combinationsthereof, and/or the like. Such models may take into consideration avariety of different inputs, including offset well data, seismic data,pilot well data, other geologic data, etc. The models and/or the inputmay be stored in the database maintained by the server and accessed bythe geological service provider.

As an example, a workflow may progress to a geology and geophysics(“G&G”) service provider, which may generate a well trajectory (see,e.g., the generation block 324), which may involve execution of one ormore G&G software packages. Examples of such software packages includethe PETREL® framework. As an example, a G&G service provider maydetermine a well trajectory or a section thereof, based on, for example,one or more model(s) provided by a formation evaluation (e.g., per theevaluation block 314), and/or other data, e.g., as accessed from one ormore databases (e.g., maintained by one or more servers, etc.). As anexample, a well trajectory may take into consideration various “basis ofdesign” (BOD) constraints, such as general surface location, target(e.g., reservoir) location, and the like. As an example, a trajectorymay incorporate information about tools, bottom-hole assemblies, casingsizes, etc., that may be used in drilling the well. A well trajectorydetermination may take into consideration a variety of other parameters,including risk tolerances, fluid weights and/or plans, bottom-holepressures, drilling time, etc.

As an example, a workflow may progress to a first engineering serviceprovider (e.g., one or more processing machines associated therewith),which may validate a well trajectory and, for example, relief welldesign (see, e.g., the validation block 328). Such a validation processmay include evaluating physical properties, calculations, risktolerances, integration with other aspects of a workflow, etc. As anexample, one or more parameters for such determinations may bemaintained by a server and/or by the first engineering service provider;noting that one or more model(s), well trajectory(ies), etc. may bemaintained by a server and accessed by the first engineering serviceprovider. For example, the first engineering service provider mayinclude one or more computing systems executing one or more softwarepackages. As an example, where the first engineering service providerrejects or otherwise suggests an adjustment to a well trajectory, thewell trajectory may be adjusted or a message or other notification sentto the G&G service provider requesting such modification.

As an example, one or more engineering service providers (e.g., first,second, etc.) may provide a casing design, bottom-hole assembly (BHA)design, fluid design, and/or the like, to implement a well trajectory(see, e.g., the design block 338). In some embodiments, a secondengineering service provider may perform such design using one of moresoftware applications. Such designs may be stored in one or moredatabases maintained by one or more servers, which may, for example,employ STUDIO® framework tools, and may be accessed by one or more ofthe other service providers in a workflow.

As an example, a second engineering service provider may seek approvalfrom a third engineering service provider for one or more designsestablished along with a well trajectory. In such an example, the thirdengineering service provider may consider various factors as to whetherthe well engineering plan is acceptable, such as economic variables(e.g., oil production forecasts, costs per barrel, risk, drill time,etc.), and may request authorization for expenditure, such as from theoperating company's representative, well-owner's representative, or thelike (see, e.g., the formulation block 334). As an example, at leastsome of the data upon which such determinations are based may be storedin one or more database maintained by one or more servers. As anexample, a first, a second, and/or a third engineering service providermay be provided by a single team of engineers or even a single engineer,and thus may or may not be separate entities.

As an example, where economics may be unacceptable or subject toauthorization being withheld, an engineering service provider maysuggest changes to casing, a bottom-hole assembly, and/or fluid design,or otherwise notify and/or return control to a different engineeringservice provider, so that adjustments may be made to casing, abottom-hole assembly, and/or fluid design. Where modifying one or moreof such designs is impracticable within well constraints, trajectory,etc., the engineering service provider may suggest an adjustment to thewell trajectory and/or a workflow may return to or otherwise notify aninitial engineering service provider and/or a G&G service provider suchthat either or both may modify the well trajectory.

As an example, a workflow can include considering a well trajectory,including an accepted well engineering plan, and a formation evaluation.Such a workflow may then pass control to a drilling service provider,which may implement the well engineering plan, establishing safe andefficient drilling, maintaining well integrity, and reporting progressas well as operating parameters (see, e.g., the blocks 344 and 348). Asan example, operating parameters, formation encountered, data collectedwhile drilling (e.g., using logging-while-drilling ormeasuring-while-drilling technology), may be returned to a geologicalservice provider for evaluation. As an example, the geological serviceprovider may then re-evaluate the well trajectory, or one or more otheraspects of the well engineering plan, and may, in some cases, andpotentially within predetermined constraints, adjust the wellengineering plan according to the real-life drilling parameters (e.g.,based on acquired data in the field, etc.).

Whether the well is entirely drilled, or a section thereof is completed,depending on the specific embodiment, a workflow may proceed to a postreview (see, e.g., the evaluation block 318). As an example, a postreview may include reviewing drilling performance. As an example, a postreview may further include reporting the drilling performance (e.g., toone or more relevant engineering, geological, or G&G service providers).

Various activities of a workflow may be performed consecutively and/ormay be performed out of order (e.g., based partially on information fromtemplates, nearby wells, etc. to fill in any gaps in information that isto be provided by another service provider). As an example, undertakingone activity may affect the results or basis for another activity, andthus may, either manually or automatically, call for a variation in oneor more workflow activities, work products, etc. As an example, a servermay allow for storing information on a central database accessible tovarious service providers where variations may be sought bycommunication with an appropriate service provider, may be madeautomatically, or may otherwise appear as suggestions to the relevantservice provider. Such an approach may be considered to be a holisticapproach to a well workflow, in comparison to a sequential, piecemealapproach.

As an example, various actions of a workflow may be repeated multipletimes during drilling of a wellbore. For example, in one or moreautomated systems, feedback from a drilling service provider may beprovided at or near real-time, and the data acquired during drilling maybe fed to one or more other service providers, which may adjust itspiece of the workflow accordingly. As there may be dependencies in otherareas of the workflow, such adjustments may permeate through theworkflow, e.g., in an automated fashion. In some embodiments, a cyclicprocess may additionally or instead proceed after a certain drillinggoal is reached, such as the completion of a section of the wellbore,and/or after the drilling of the entire wellbore, or on a per-day, week,month, etc. basis.

Well planning can include determining a path of a well that can extendto a reservoir, for example, to economically produce fluids such ashydrocarbons therefrom. Well planning can include selecting a drillingand/or completion assembly which may be used to implement a well plan.As an example, various constraints can be imposed as part of wellplanning that can impact design of a well. As an example, suchconstraints may be imposed based at least in part on information as toknown geology of a subterranean domain, presence of one or more otherwells (e.g., actual and/or planned, etc.) in an area (e.g., considercollision avoidance), etc. As an example, one or more constraints may beimposed based at least in part on characteristics of one or more tools,components, etc. As an example, one or more constraints may be based atleast in part on factors associated with drilling time and/or risktolerance.

As an example, a system can allow for a reduction in waste, for example,as may be defined according to LEAN. In the context of LEAN, considerone or more of the following types of waste: Transport (e.g., movingitems unnecessarily, whether physical or data); Inventory (e.g.,components, whether physical or informational, as work in process, andfinished product not being processed); Motion (e.g., people or equipmentmoving or walking unnecessarily to perform desired processing); Waiting(e.g., waiting for information, interruptions of production during shiftchange, etc.); Overproduction (e.g., production of material,information, equipment, etc. ahead of demand); Over Processing (e.g.,resulting from poor tool or product design creating activity); andDefects (e.g., effort involved in inspecting for and fixing defectswhether in a plan, data, equipment, etc.). As an example, a system thatallows for actions (e.g., methods, workflows, etc.) to be performed in acollaborative manner can help to reduce one or more types of waste.

As an example, a system can be utilized to implement a method forfacilitating distributed well engineering, planning, and/or drillingsystem design across multiple computation devices where collaborationcan occur among various different users (e.g., some being local, somebeing remote, some being mobile, etc.). In such a system, the varioususers via appropriate devices may be operatively coupled via one or morenetworks (e.g., local and/or wide area networks, public and/or privatenetworks, land-based, marine-based and/or areal networks, etc.).

As an example, a system may allow well engineering, planning, and/ordrilling system design to take place via a subsystems approach where awellsite system is composed of various subsystem, which can includeequipment subsystems and/or operational subsystems (e.g., controlsubsystems, etc.). As an example, computations may be performed usingvarious computational platforms/devices that are operatively coupled viacommunication links (e.g., network links, etc.). As an example, one ormore links may be operatively coupled to a common database (e.g., aserver site, etc.). As an example, a particular server or servers maymanage receipt of notifications from one or more devices and/or issuanceof notifications to one or more devices. As an example, a system may beimplemented for a project where the system can output a well plan, forexample, as a digital well plan, a paper well plan, a digital and paperwell plan, etc. Such a well plan can be a complete well engineering planor design for the particular project.

FIG. 8 shows a schematic diagram depicting an example of a drillingoperation of a directional well in multiple sections. As an example, oneor more actions performed during drilling of the directional well can beperformed using information in a digital well plan. The drillingoperation depicted in FIG. 8 includes a wellsite drilling system 800 anda field management tool 820 for managing various operations associatedwith drilling a bore hole 850 of a directional well 817. The wellsitedrilling system 800 includes various components (e.g., drillstring 812,annulus 813, bottom hole assembly (BHA) 814, kelly 815, mud pit 816,etc.). As shown in the example of FIG. 8, a target reservoir may belocated away from (as opposed to directly under) the surface location ofthe well 817. In such an example, special tools or techniques may beused to ensure that the path along the bore hole 850 reaches theparticular location of the target reservoir.

As an example, the BHA 814 may include sensors 808, a rotary steerablesystem 809, and a bit 810 to direct the drilling toward the targetreservoir guided by a pre-determined survey program for measuringlocation details in the well. As an example, a digital well plan caninclude trajectory information that can be used for guiding the bit 810to the target reservoir. As an example, the subterranean formationthrough which the directional well 817 is drilled may include multiplelayers (not shown) with varying compositions of material, geophysicalcharacteristics, and geological conditions. Both the drilling planningduring the well design stage and the actual drilling according to thedrilling plan in the drilling stage may be performed in multiplesections (e.g., sections 801, 802, 803 and 804) corresponding to themultiple layers in the subterranean formation. For example, certainsections (e.g., sections 801 and 802) may use cement 807 reinforcedcasing 806 due to the particular formation compositions, geophysicalcharacteristics, and geological conditions.

In the example of FIG. 8, a surface unit 811 may be operatively linkedto the wellsite drilling system 800 and the field management tool 820via communication links 818. The surface unit 811 may be configured withfunctionalities to control and monitor the drilling activities bysections in real-time via the communication links 818. The fieldmanagement tool 820 may be configured with functionalities to storeoilfield data (e.g., historical data, actual data, surface data,subsurface data, equipment data, geological data, geophysical data,target data, anti-target data, etc.) and determine relevant factors forconfiguring a drilling model and generating a drilling plan. Theoilfield data, the drilling model, and the drilling plan may betransmitted via the communication link 818 according to a drillingoperation workflow. The communication links 818 may include acommunication subassembly. As an example, the field management tool 820can include one or more features of the system 770 of the example ofFIG. 7 (see, e.g., the processor(s) 772, the memory 774, theinstructions 776, the interface(s) 778, etc.).

During various operations at a wellsite, data can be acquired foranalysis and/or monitoring of one or more operations. Such data mayinclude, for example, subterranean formation, equipment, historicaland/or other data. Static data can relate to, for example, formationstructure and geological stratigraphy that define the geologicalstructures of the subterranean formation. Static data may also includedata about a bore, such as inside diameters, outside diameters, anddepths. Dynamic data can relate to, for example, fluids flowing throughthe geologic structures of the subterranean formation over time. Thedynamic data may include, for example, pressures, fluid compositions(e.g. gas oil ratio, water cut, and/or other fluid compositionalinformation), and states of various equipment, and other information.

The static and dynamic data collected via a bore, a formation,equipment, etc. may be used to create and/or update a three dimensionalmodel of one or more subsurface formations. As an example, static anddynamic data from one or more other bores, fields, etc. may be used tocreate and/or update a three dimensional model. As an example, hardwaresensors, core sampling, and well logging techniques may be used tocollect data. As an example, static measurements may be gathered usingdownhole measurements, such as core sampling and well loggingtechniques. Well logging involves deployment of a downhole tool into thewellbore to collect various downhole measurements, such as density,resistivity, etc., at various depths. Such well logging may be performedusing, for example, a drilling tool and/or a wireline tool, or sensorslocated on downhole production equipment. Once a well is formed andcompleted, depending on the purpose of the well (e.g., injection and/orproduction), fluid may flow to the surface (e.g., and/or from thesurface) using tubing and other completion equipment. As fluid passes,various dynamic measurements, such as fluid flow rates, pressure, andcomposition may be monitored. These parameters may be used to determinevarious characteristics of a subterranean formation, downhole equipment,downhole operations, etc.

FIG. 9 shows an example of a system 900 that includes various componentsthat can be local to a wellsite and includes various components that canbe remote from a wellsite. As shown, the system 900 includes a block902, a block 904, a block 906 and an equipment block 908. These blockscan be labeled in one or more manners other than as shown in the exampleof FIG. 9. In the example of FIG. 9, the blocks 902, 904, 906 and 908can be defined by one or more of operational features, functions,relationships in an architecture, etc.

As an example, the block 902 can be associated with a well managementlevel (e.g., well planning and/or orchestration) and can be associatedwith a rig management level (e.g., rig dynamic planning and/ororchestration). As an example, the block 904 can be associated with aprocess management level (e.g., rig integrated execution). As anexample, the block 906 can be associated with a data management level(e.g., sensor, instrumentation, inventory, etc.). As an example, theequipment block 908 can be associated with a wellsite equipment level(e.g., wellsite subsystems, etc.).

In the example of FIG. 9, the block 902 includes a plan/replan block922, an orchestrate/arbitrate block 924 and a local resource managementblock 926. In the example of FIG. 9, the block 904 includes anintegrated execution block 944, which can include or be operativelycoupled to blocks for various subsystems of a wellsite such as adrilling subsystem, a mud management subsystem (e.g., a hydraulicssubsystem), a casing subsystem (e.g., casings and/or completionssubsystem), and, for example, one or more other subsystems. In theexample of FIG. 9, the block 906 includes a data management andreal-time services block 964 (e.g., real-time or near real-timeservices) and a rig and cloud security block 968. In the example of FIG.9, the equipment block 908 is shown as being capable of providingvarious types of information to the block 906. For example, considerinformation from a rig surface sensor, a LWD/MWD sensor, a mud loggingsensor, a rig control system, rig equipment, personnel, material, etc.In the example, of FIG. 9, a block 970 can provide for one or more ofdata visualization, automatic alarms, automatic reporting, etc. As anexample, the block 970 may be operatively coupled to the block 906and/or one or more other blocks.

As mentioned, a portion of the system 900 can be remote from a wellsite.For example, to one side of a dashed line appear a remote operationcommand center block 992, a database block 993, a drilling workflowframework block 994, an enterprise resource planning (ERP) block 995 anda field services delivery block 996. Various blocks that may be remotecan be operatively coupled to one or more blocks that may be local to awellsite system. For example, a communication link 912 is illustrated inthe example of FIG. 9 that can operatively couple the blocks 906 and 992(e.g., as to monitoring, remote control, etc.), while anothercommunication link 914 is illustrated in the example of FIG. 9 that canoperatively couple the blocks 906 and 996 (e.g., as to equipmentdelivery, equipment services, etc.). Various other examples of possiblecommunication links are also illustrated in the example of FIG. 9.

In the example of FIG. 9, various blocks can be components that maycorrespond to one or more software instruction sets (e.g.,processor-executable instructions, add-ons, plug-ins, etc.), hardwareinfrastructure, firmware, equipment, or a combination thereof.Communication between the components may be local or remote, direct orindirect, via application programming interfaces, and procedure calls,or through one or more communication channels.

As an example, the block 906 (e.g., a core and services block) caninclude functionality to manage individual pieces of equipment and/orequipment subsystems. As an example, such a block can includefunctionality to handle basic data structure of the oilfield, such asthe rig, acquire metric data, produce reports, and manages resources ofpeople and supplies. As an example, such a block may include a dataacquirer and aggregator, a rig state identifier, a real-time (RT) drillservices (e.g., near real-time), a reporter, a cloud, and an inventorymanager.

As an example, a data acquirer and aggregator can include functionalityto interface with individual equipment components and sensor and acquiredata. As an example, a data acquirer and aggregator may further includefunctionality to interface with sensors located at the oilfield.

As an example, a rig state identifier can includes functionality toobtain data from the data acquirer and aggregator and transform the datainto state information. As an example, state information may includehealth and operability of a rig as well as information about aparticular task being performed by equipment.

As an example, RT drill services can include functionality to transmitand present information to individuals. In particular, the RT drillservices can include functionality to transmit information toindividuals involved according to roles and, for example, device typesof each individual (e.g., mobile, desktop, etc.). In one or moreembodiments, information presented by RT drill services can be contextspecific, and may include a dynamic display of information so that ahuman user may view details about items of interest.

As an example, a wellsite “cloud” framework can correspond to aninformation technology infrastructure locally at an oilfield, such as anindividual rig in the oilfield. In such an example, the wellsite “cloud”framework may be an “Internet of Things” (IoT) framework. As an example,a wellsite “cloud” framework can be an edge of the cloud (e.g., anetwork of networks) or of a private network.

In the example of FIG. 9, the equipment block 908 can correspond tovarious controllers, control unit, control equipment, etc. that may beoperatively coupled to and/or embedded into physical equipment at awellsite such as, for example, rig equipment. For example, the equipmentblock 908 may correspond to software and control systems for individualitems on the rig. As an example, the equipment block 908 may provide formonitoring sensors from multiple subsystems of a drilling rig andprovide control commands to multiple subsystem of the drilling rig, suchthat sensor data from multiple subsystems may be used to provide controlcommands to the different subsystems of the drilling rig and/or otherdevices, etc. For example, a system may collect temporally and depthaligned surface data and downhole data from a drilling rig and transmitthe collected data to data acquirers and aggregators in core services,which can store the collected data for access onsite at a drilling rigor offsite via a computing resource environment.

As an example, a system can include a framework that can acquire datasuch as, for example, real-time data associated with one or moreoperations such as, for example, a drilling operation or drillingoperations. As an example, consider the PERFORM™ toolkit framework(Schlumberger Limited, Houston, Tex.).

As an example, a service can be or include one or more of OPTIDRILL™,OPTILOG™ and/or other services marketed by Schlumberger Limited,Houston, Tex.

The OPTIDRILL™ technology can help to manage downhole conditions and BHAdynamics as a real-time drilling intelligence service. The service canincorporate a rigsite display (e.g., a wellsite display) of integrateddownhole and surface data that provides actionable information tomitigate risk and increase efficiency. As an example, such data may bestored, for example, to a database system (e.g., consider a databasesystem associated with the STUDIO™ framework).

The OPTILOG™ technology can help to evaluate drilling system performancewith single- or multiple-location measurements of drilling dynamics andinternal temperature from a recorder. As an example, post-run data canbe analyzed to provide input for future well planning.

As an example, information from a drill bit database may be accessed andutilized. For example, consider information from Smith Bits(Schlumberger Limited, Houston, Tex.), which may include informationfrom various operations (e.g., drilling operations) as associated withvarious drill bits, drilling conditions, formation types, etc.

As an example, one or more QTRAC services (Schlumberger Limited, HoustonTex.) may be provided for one or more wellsite operations. In such anexample, data may be acquired and stored where such data can includetime series data that may be received and analyzed, etc.

As an example, one or more M-I SWACO™ services (M-I L.L.C., Houston,Tex.) may be provided for one or more wellsite operations. For example,consider services for value-added completion and reservoir drill-influids, additives, cleanup tools, and engineering. In such an example,data may be acquired and stored where such data can include time seriesdata that may be received and analyzed, etc.

As an example, one or more ONE-TRAX™ services (e.g., via the ONE-TRAXsoftware platform, M-I L.L.C., Houston, Tex.) may be provided for one ormore wellsite operations. In such an example, data may be acquired andstored where such data can include time series data that may be receivedand analyzed, etc.

As mentioned, for a project that aims to develop or further develop areservoir, a method can include generating a set of possibletrajectories, which may be referred to as a set of candidatetrajectories, and interrogating one or more of the candidates withrespect to one or more performance indicators (PIs).

As an example, a wellbore trajectory can be defined as a geometric tracethat connects a well surface location and one or more target locations.As an example, when a wellbore trajectory is designed by a drillingengineer, several rules (e.g., performance indicators (PIs)) may bespecified that the trajectory may be expected to follow. For example,consider one or more of a kickoff point that is to be below a mudline,collision avoidance with one or more existing wells, and dogleg severity(DLS), which may be provided as an indicator of an ability for drillingtools to turn, not get stuck, etc.

As an example, a method may be implemented as an automated orsemi-automated way to design one or more trajectories. As an example, amethod may design a single trajectory or a group of trajectories, forexample, without collision avoidance. As an example, a method caninclude modeling one or more trajectories as a multi-objectiveoptimization problem. For example, PIs can be utilized as cost functionsand rules as constraints.

As an example, a multi-objective optimization problem can be solvedusing a Pareto frontier approach (e.g., as to generation, filtering,etc. of candidate trajectories). A planning objective can be to design awell trajectory that is drillable with its PIs being the “best”possible; in other words, a planning objective can be to determine aplanned trajectory that can be drilled with some assurances of low costand risk. In a multi-objective optimization problem approach, complexitycan mean that a unique solution may not exist. In the context oftrajectory planning, complexity can arise due to PIs that may contradicteach other. For example, less DLS and less total depth can be two PIs;however, less DLS can result in a greater total depth. In such ascenario, a trade-off may be made that aims to favor one over the other.In a Pareto frontier approach, a Pareto efficient set of candidatetrajectories can be a set where associated PIs for individual members ofthe set are not “worse” than those of other individual members of theset. As an example, a Pareto efficient set may be determined using aniterative process that can include, for example, implementation of oneor more evolutionary processes, which may provide for a more globalsolution.

As an example, the Pareto principle may be explained as an 80-20phenomenon, where around 80 percent of effects result from around 20percent of the causes. As an example, an approach can include analysisof information that can be considered as lying outside the Pareto curve,which may include relatively rare events, which may have effects on anundetermined scale. For example, a method can include analyzinginformation from drilling operations for a so-called “black swan” or“black swans”, which may pertain to particular scenario that can becharacterized generally according to a Pareto frontier.

In terms of a characterization approach, a black swan can becharacterized based on one or more of a scenario being: a rare eventthat is beyond the realm of ordinary expectations (e.g., as in Paretoprinciple); having a generally non-computable probability usingscientific methods (owing to the nature of small probabilities); andassociated with a psychological bias as to uncertainty and to a rareevent's role in operational outcomes.

FIG. 10 shows an example of a method 1000 that includes a receptionblock 1010 for receiving a well plan (e.g., a digital well plan via anetwork interface, etc.), an issue block 1020 for issuing at least oneinstruction to drill the well based at least in part on the well plan(e.g., one or more control instructions in the digital well plan, etc.),a comparison block 1030 for comparing acquired information of actualdrilling of the well with information of the well plan (e.g., at leastin part via receipt of digital information and comparison to digitalinformation of the well plan in digital form), and an output block 1040for outputting results of the comparison (e.g., storing informationbased on the comparison to a digital storage medium or media).

As an example, the method 1000 can include, per block 1010, receiving adigital well plan; per block 1020, issuing drilling instructions fordrilling a well based at least in part on the digital well plan; perblock 1030, comparing acquired information associated with drilling ofthe well with well plan information of the digital well plan; and perblock 1040, outputting results based at least in part on the comparingof the acquired information with the well plan information.

As shown in the example of FIG. 10, the reception block 1010 can includereceiving trajectory information 1012, receiving bottom hole assembly(BHA) information 1014, receiving operational parameters (e.g., and/oroperational parameter values) 1016 and optionally receiving one or moreother types of information 1018. Such information can be part of adigital well plan that can be stored in a digital medium or media and,for example, received by a computing system via an interface, which maybe a network interface.

As shown in the example of FIG. 10, the issue block 1020 can includeacquiring information during one or more drilling operations 1022,digitizing observations 1024 and/or one or more other forms ofinformation acquisition. In the example of FIG. 10, the acquiredinformation during drilling operation 1022 can include informationassociated with one or more of the types of information of blocks 1012,1014, 1016 and 1018. As an example, information may be for a formation,an operation, a piece of equipment, an amount of time for an operation,an amount of time that is not for an operation (e.g., an amount ofnon-productive time (NPT), etc.), etc.

As shown in the example of FIG. 10, the comparison block 1030 mayinclude performing a sensitivity analysis 1032, generating a chart 1034(e.g., consider a tornado chart, etc.), performing machine learning 1036(e.g., neural network model-based learning, etc. to generate a trainedmachine model) and/or one or more other types of comparison analyses. Asan example, an analysis may aim to determine what type equipment,operational parameters, etc. led to a favorable condition(s) and/or toan unfavorable condition(s). For example, where sticking is anunfavorable condition, a machine learning approach may analyze variousfactors using a trained machine model of drilling a well in a formationto identify a most probable cause of the sticking (e.g., BHA relatedcause, drilling fluid related cause, rate of penetration related cause,etc.). Thus, a comparison result may be based on an analysis that ismore involved than comparison of two numbers (e.g., a planned numberversus an actual number). As an example, comparison results can includeinvolved analysis results and comparison of numeric values results. Asan example, where machine learning is utilized, a machine learning modelcan learn based on results from performing analyses of planned versusactual results to generate a trained machine model. As an example, ananalysis may include performing one or more statistical analyses, whichmay be in addition to a machine learning approach and/or one or moreother analyses. In the example of FIG. 10, the comparison block 1030 canbe automated by one or more computing systems.

As to a machine learning approach, as an example, one or more artificialneural networks (ANNs) can be constructed according to an architecturethat includes nodes or neurons and may include layers and/or otherfeatures. ANNs include interconnections between various neurons and caninclude discrete layers, connections, and directions of datapropagation.

As an example, an ANN can take input from an operation or operations andfeed the input to a first layer of the ANN where, in the first layer,individual neurons can pass the data to a second layer. In such anexample, the second layer of neurons can perform one or more tasks andpass along information to a next layer, which may be anotherintermediate layer or a final layer, where a result or results areoutput.

In an ANN, each neuron can assign a weighting to its input, for example,an indication as to how “true” or “false” it is relative to the taskbeing performed. The final output can be determined by a total of thoseweightings. As an example, consider attributes of a condition such assticking during drilling (e.g., stuck pipe condition) being processed byneurons, which may consider aspects such as length of pipe, diameter ofpipe, mud-flow rate, severity of a trajectory (e.g., dogleg severity(DLS)), weight on bit (WOB), etc. The ANN's task can be to concludewhether the input is indicative of sticking or not. The ANN can generatea probability vector that is based on weightings. For example, theoutput from an ANN might be a confidence number or confidence numbers,where possible other outcomes exist (e.g., 86 percent confident theinput is not indicative of sticking).

In the foregoing example, during use, if the ANN fails to output highconfidence percentages to input associated with sticking, the ANN may belacking in its training. In other words, if the ANN was trained usingpoor quality input and/or poor labeling of features in that input, thenthe ANN may be expected to perform poorly when implemented for purposesof recognition of sticking of drill pipe during a drilling operation.

Training of an ANN can be a complex process, particularly as toaccessing appropriate training data. As an example, training data may beaccessed and/or generated using one or more techniques. For example,consider a model of a drilling operation that can be utilized togenerate training data for inputs that lead to model-based results thatinclude sticking. As an example of real training data, consider adatabase of drilling data and outcomes where the outcomes includesticking. Such a database may be accessed for purposes of training amachine model such as an ANN. As an example, a machine model can belocal for a particular field where the field can include offset wellssuch that, as wells are drilled, information from those drillingoperations and associated outcomes can be utilized for purposes oftraining a machine model to generate a trained machine model, which maybe updated as the number of wells in the field increases. In such anexample, modeling such as simulation of drilling can be performed togenerate training data, where modeling may optionally be updated to bemore accurate as information is acquired from drilling operations (e.g.,as to conditions, layers, material, etc.). Depending on architecture, anANN may demand thousands of input/outcome sets of training data untilthe weightings of the neuron inputs are tuned precisely such that a“trained” ANN is generated. In the foregoing example, a properly trainedANN “knows”, through appropriate training, what input is likely to giverise to an outcome (e.g., sticking, etc.).

As the number of neurons and layers increase, an ANN may be referred toas a “deep” ANN, which demands deep learning. Deep learning can bedefined as, for example, a class of machine learning algorithms that:(a) use a cascade of multiple layers of nonlinear processing units forfeature extraction and transformation where each successive layer usesthe output from the previous layer as input; (b) learn in supervised(e.g., classification) and/or unsupervised (e.g., pattern analysis)manners; and (c) learn multiple levels of representations thatcorrespond to different levels of abstraction where the levels form ahierarchy of concepts.

As an example, a framework can include one or more features of theTENSORFLOW framework (Google, Mountain View, Calif.) framework, whichincludes a software library for dataflow programming that provides forsymbolic mathematics, which may be utilized for machine learningapplications such as artificial neural networks (ANNs), etc.

As an example, a digital well plan can include information that cancorrespond to information germane to a machine model. For example,consider a digital well plan that includes specifications of a BHAutilized to drill a well. As an example, a machine model can include anarchitecture with one or more nodes (e.g., neurons) that can process oneor more BHA specifications as part of an analysis to output informationas to an outcome or outcomes. As an example, a digital well plan caninclude information that can be utilized in a simulation of drilling awell. In such an example, information generated during the simulationcan be input to a trained machine model, which can, for example, outputinformation as to one or more outcomes. For example, consider asimulation that outputs information that can be utilized as input to atrained machine model for purposes of determining likelihood ofsticking. In such an example, output from the trained machine model maybe utilized to revise the digital well plan, for example, prior to orbefore implementation of one or more portions of the digital well plan(e.g., for a particular section of a well, etc.). As an example, wheremultiple offset wells are to be drilled, information output by a trainedmachine model may be utilized to generate and/or revise a digital wellplan for one or more of the multiple offset wells.

As an example, output from a trained machine model can provideinformation as to whether a scenario may be outside of a Paretoscenario. In such an example, a method can include determining whetherthe scenario includes indicia of a “black swan” scenario, as may be in adatabase that includes information from actual drilling operationsand/or information from simulated drilling scenarios.

As an example, the comparison block 1030 of the method 1000 can includetime for time analyses (e.g., as to productive time, non-productivetime, etc.). As an example, time and actual physical parameters mayallow for comparison results to indicate wear of one or more pieces ofequipment. In such an example, the wear may be taken into account whenplanning another well that is to be drilled using one or more pieces ofequipment that were used to drill the well (e.g., and/or one or moreother wells). As an example, comparison results may be stored inassociation with one or more pieces of equipment such as, for example, apiece or pieces of a bottom hole assembly, which may be tagged using oneor more techniques, technologies, etc. For example, a piece of equipmentmay include an RFID, a bar code or other type of identifier. In such anexample, upon use of the piece of equipment, storage of the piece ofequipment, movement of the piece of equipment, a reader may identify thepiece of equipment and access its history, which can include priorlocation(s), wear and/or one or more other performance related types ofinformation. Such information may optionally be input to a computingsystem for purposes of planning a well, drilling a well, revising a wellplan, etc.

As mentioned, one or more machine learning approaches may be utilized.As to the comparison block 1030, the machine learning block 1036 caninclude implementing one or more machine models, for example, as intraining one or more machine models and/or use of one or more trainedmachine models.

As to the output block 1040, in the example of FIG. 10, the results maybe output in one or more manners. As an example, results may be outputin a batch manner, for example, once drilling has been completed for thewell according to the well plan and/or in a manner that occurs duringdrilling of the well. As to the latter, the comparison block 1030 may beimplemented during the drilling of the well per the issue block 1020such that results are generated and optionally output in real-time ornear-real-time. In such an example, the well plan may optionally berevised based at least in part on the comparison results or a portionthereof. As an example, the method 1000 of FIG. 10 may be part of areal-time control system for drilling of a well at least in partaccording to a digital well plan.

As to the method 1000 of FIG. 10, at some point in time, the plannedwell is drilled, which can be in a manner that deviates from aninitially received well plan due to one or more circumstances, which caninclude learning from comparison(s) of deviation(s) from the initiallyreceived well plan and actually drilling (e.g., which may includeassociating drilling operations with favorable productive time and/orunfavorable non-productive time, or factors that lead to a reduction inunfavorable non-productive time). Once drilled, comparison results forthe drilled well may be classified as comparison results for plannedversus actual for the drilled well; noting that such results may beoutput and stored as partial results during actual drilling of the well.At one or more points in time, the comparison results for the well maybe considered comparison results for an “offset well” with respect toanother well, which may be a well that has yet to be drilled or that hascommenced drilling. In such an example, as explained below, thecomparison results can be utilized for generating and/or revising a wellplan for another well or wells.

As an example, a GUI can include a scene of a subterranean environmentwhere a trajectory can be rendered, for example, as a graphic from asurface location to a target location.

As an example, a method may be implemented in a manner that allows auser to interact with a computing system to investigate one or moreadjustments to a well plan that is under design or that is beingutilized for drilling.

As an example, a method can include utilization of one or moreperformance indicators (PIs) where a PI can include one or more of totallength of a trajectory, a maximum rate of curvature (dogleg severity) ofa trajectory, a depth of a kick-off point, a measure of ananti-collision risk (e.g., such as an oriented separation factor), ameasure of a size of a mud-weight window, a measure of an averagefriction along a bore, etc.

As an example, one or more constraints can include one or more of aminimum depth of a kick-off point constraint, a maximum allowableanti-collision risk constraint, a maximum allowable rate of curvature ofa trajectory constraint, a maximum allowable rate of curvature of atrajectory as a function of depth constraint, etc.

As an example, a constraint may be a physical constraint and/or a rightsconstraint. As an example, a physical constraint may pertain to one ormore formation characteristics. As an example, a rights constraint maypertain to property and/or resource rights, which may be defined by aphysical boundary or physical boundaries. For example, rights maypertain to one or more leasehold rights, one or more leaselines, etc.Such a type of constraint may be implemented as a physical boundary orphysical boundaries and/or as a time factor or time factors, where aright or rights may be associated with time (e.g., consider adevelopment time, a production time, etc.).

In the example of FIG. 10, the output of the output block 1040 may beutilized to determine one or more factors as to drilling another well.Such factors can include factors that are “positive” and may includefactors that are “negative”. Positive factors can include what toinclude in the plan as to operations, equipment, etc.; whereas, negativefactors can include what not do as to operations, equipment, etc. Forexample, a negative factor may be “do not include component XYZ in thebottom hole assembly for a well that is to be drilled within a radius ofR from well ZZZ”. In such an example, a factor or factors from an“offset” well that has at least in part been drilled can be utilized forgenerating a well plan and/or actual drilling of another well, which maybe in the same basin or not.

The method 1000 is shown in FIG. 10 in association with variouscomputer-readable medium (CRM) blocks 1011, 1021, 1031, and 1041. Suchblocks generally include instructions suitable for execution by one ormore processors (or cores) to instruct a computing device or system toperform one or more actions. While various blocks are shown, a singlemedium may be configured with instructions to allow for, at least inpart, performance of various actions of the method 1000. As an example,a computer-readable medium (CRM) may be a computer-readable storagemedium that is non-transitory and not a carrier wave. As an example, oneor more of the CRM block 1011, 1021, 1031 and 1041 can includeinstructions that can be instructions of a system such as, for example,the system 770 of FIG. 7 (see, e.g., the instructions 776). As anexample, the method 1000 of FIG. 10 may be implemented at least in partby a system such as the system 770 of FIG. 7.

FIG. 11 shows an example of a method 1110 that pertains to training aneural network as an example of a training a machine model and a method1130 that pertains to using a trained neural network as an example ofusing a trained machine model.

The method 1110 includes an access block 1114 for accessing a deltadatabase from deviations from digital well plan(s) and associatedoutcome(s), a train block 1118 for training a neural network as amachine model to generate a trained neural network as a trained machinemodel, and an output block 1122 for outputting the trained neuralnetwork as the trained machine model.

The method 1130 includes a reception block 1134 for receiving delta(s)from a drilling operation, an input block 1138 for inputting thereceived delta(s) from the drilling operation to the trained neuralnetwork (the trained machine model) to generate output, and anadjustment block 1142 for adjusting the drilling operation and/or adigital well plan using the output.

In the example of FIG. 11, the deltas represent deviations from acorresponding digital well plan. Such deviations can be with respect toone or more of equipment, operation of equipment, timing of operationsof equipment, etc. As an example, consider a digital well plan for awell that includes information to ramp up a pump rate of pumpingdrilling fluid where actual information from the rigsite indicates thatthe ramp up of the pump rate of pumping the drilling fluid deviated asto the ramp up profile. In such an example, consider a deviation as toduration for the ramp up and/or the final pump rate of pumping thedrilling fluid. Such a deviation or deviations can be a delta or deltas.As an example, a trained neural network can be trained based on trainingdata as to deltas such as deviations in ramp up with associatedoutcomes. In the foregoing example, the trained neural network can takethe delta of the drilling operation as input and output a likelyoutcome. Where the outcome is a beneficial (e.g., positive) outcome, thedrilling operation may continue with the deviation; however, where theoutcome is a detrimental outcome (e.g., negative), the drillingoperation can be adjusted such as by controlling one or more pieces ofequipment and/or by adjusting the digital well plan for the well.

In the examples of FIG. 11, control can be effectuated based on actualdeviations from digital well plans where such deviations may bepositive, neutral or negative. As an example, a method may includeaccessing a database to identify one or more deltas that match a deltaexperienced during a drilling operation. Such a method may operateseparately or in combination with the method 1110 and/or the method1130.

As an example, a deviation from a digital well plan may pertain tolocation of a centralizer that acts to centralize a portion of a drillstring in a bore. A centralizer can be a device fitted with a hingedcollar and bowsprings to keep a portion of a drill string in the centerof a bore. As to casing, if a casing string is cemented off-center,there can be a high risk that a channel of drilling fluid orcontaminated cement will be left where the casing contacts theformation, creating an imperfect seal.

As an example, a digital well plan may call for locating a centralizerat a particular location (e.g., z-location such as zc1) along a drillstring that is being utilized to drill a bore in a geologic formationaccording to a trajectory specified by the digital well plan. In such anexample, where the z-location is not zc1 but rather zc1 plus a distancezd, then the delta can be a numeric value such as the distance zd and/orthe zc1 plus the distance zd, which may be referenced with respect to acomponent of the drill string such as a bit. The delta can be utilizedto search a database of deltas and/or be utilized as input to a trainedmachine model such that a likely outcome can be determined for thedelta. If the outcome is indicated as being detrimental, if the locationcannot be adjusted, the outcome may be addressed via control of thedrilling operation. Where the outcome is indicated as being beneficial,the benefit thereof may be addressed by optionally adjusting one or moreactions of the digital well plan to take advantage of the benefit.

In various examples, a method may assume that a digital well plan issufficient for a drilling operation. As such, where the drillingoperation follows the plan, there may be no call for analysis. However,where a deviation occurs, a method can call for analysis to determinewhether that deviation is likely to have an outcome that is beneficial,neutral or negative.

As an example, a digital well plan can include automated instructionsfor a piece of equipment. In such an example, during a drillingoperation, information may be acquired at a rigsite that indicates thatthe piece of equipment operated in a manner that differed from theautomated instructions. In such an example, as the instructions areautomated instructions that are likely to have no to minimal impact froma human operator, the information acquired may indicate that the pieceof equipment and/or a transmission channel to that piece of equipmentmay have an issue. In such an example, an analysis may indicate that adelta is associated with a potential equipment issue, rather than humanaction that caused a deviation from the digital well plan.

As an example, a delta can be a human action delta in that a humanaction is a direct cause of the delta. For example, where a digitaldrill plan calls for a mud pump flow rate of X to be set by a humanoperator and where the actual mud pump flow rate is set to Y, then thedeviation is a human action delta.

As another example, consider a digital well plan that includesinformation as to how to mix mud (a recipe for drilling fluid) where ahuman operator deviates from the information in the digital well plan.In such an example, the human operator may proceed to follow how to pumpthe mud as set forth in the digital well plan or may adjust how to pumpthe mud based on experience. As to the latter, a delta generated by thelack of adherence to mud recipe may be flagged as a possible reason (oroutcome) as to why the human operator deviated from the digital wellplan on how to pump the mud. In such a scenario, a system can includegenerating output from input delta(s) and rendering one or more GUInotices to a display at a rigsite where the notices may indicate that(i) the how to mix mud was not followed and (ii) the how to pump the mudwas not followed. In such an example, the human operator may interactwith the GUI to confirm the notices, which may provide feedback thatwhere a human operator deviates from how to mix mud, that human operatormay deviate from how to pump the mud.

As explained, deltas can be equipment deltas or human deltas, orpossibly a combination of equipment and human. Where deltas are humanrelated, such deltas may be associated with a human operator or a teamof operators. Such associations can be utilized, for example, asknowledge that a particular human operator and/or a team of operatorsmay deviate from a digital well plan as to one or more aspects of thatdigital well plan. As such an operator or team may be experienced, theymay be assumed to handle such deltas. However, where one or more othertypes of deltas arise for such an operator or team, those may be flaggedas being of a heightened concern and analysis (e.g., “you do notnormally deviate from that portion of the digital well plan, pleaseconsider X, Y and Z”).

As to an equipment delta, that may be associated with one or more piecesof equipment. For example, a rig may be utilized to drill one or morewells using one or more common pieces of equipment. A piece of equipmentthat is flagged as generating a delta from a digital well plan may besubjected to repair, replacement, calibration, etc. As an example, wherethe piece of equipment is deemed adequate for use even though it throwsa delta, the delta may be filtered out, particularly where the delta isassociated with a neutral outcome. If an equipment delta is associatedwith a negative outcome, the piece of equipment may be subjected tointervention, such as being replaced or recalibrated, if possible.

As explained above, the use of deltas based on information in a digitalwell plan and actual information can improve drilling operations.Further, a delta-based approach can diminish the amount of informationthat demands analysis. For example, if a drilling operation deviatesfrom a digital well plan in four instances, then four instances ofanalysis may be called for and corresponding results returned to adriller or drillers. Ultimately, a digital well plan that operateswithout a delta may be akin to a “no-hitter” or a “perfect game” inbaseball in that everything went according to the digital well plan.

As deltas become understood better, digital well plans can be improved.As an example, deltas can give become a basis for operational bounds ina digital well plan (e.g., consider neutral outcomes). For example,where bounds are specified and actual parameter values fall within thosebounds, delta generation may be decreased.

As a delta-based approach is utilized, evolution can be expected suchthat digital well plans improve and the number of deltas experienced perdrilling operation diminishes. Over time, concepts such as the Paretofrontier and black swans can become more apparent, which, in turn, canbe utilized to improve drilling operations.

FIG. 12 shows an example of a method 1200 that includes a commencementblock 1210 for commencing a well planning process to generate a wellplan for drilling a well, a reception block 1220 for receivinginformation for the well, a selection block 1230 for selecting one ormore offset wells (e.g., according to one or more factors such as adistance, a lateral length, a total depth, a type of BHA, etc.), anaccess and/or generate block 1240 for accessing and/or generatingcomparison results for one or more actual drilled offset wells withrespect to one or more corresponding well plan(s) for one or morecorresponding drilled offset wells, a generation block 1250 forgenerating a well plan for the well based at least in part on at least aportion of the comparison results for at least one of the one or moreoffset wells, and a drill block 1260 for drilling the well based atleast in part on the generated well plan. As shown in the example ofFIG. 12, the method 1200 can optionally include a decision block 1270for deciding whether more offset results are available for one or moreoffset wells (e.g., an offset well of the block 1230 and/or anotheroffset well) and a revision block 1280 for optionally revising the wellplan based at least in part on additional comparison results. In such anexample, the method 1200 may operate in real-time in a field wide manneras the field is being developed at least in part through drilling ofwells.

As an example, comparison results may be revised based on post-drillinginformation such as, for example, production information of a well,fracturing information of a well, completion stability information of awell, etc. For example, where production is favorable, an analysis maybe performed to identify one or more factors that gave rise to thefavorable production, which can include drilling factors. Such factorsmay be indicated as being favorable, for example, via a machine learningmodel that is trained at least in part on post-drilling production data.In such an example, comparison results may be utilized when generatingand/or revising a well plan for a well to achieve more favorablepost-drilling production data from the well.

As to a completion, a method may assess trajectory tortuosity (e.g.,angles, doglegs, etc.) and utilize a sensitivity analysis that linkstrajectory to one or more portions of a completion (e.g., casing,tubing, cementing etc.). In such an example, where an actual drillingoperation results in a particular actual trajectory that differs from awell plan, an update may be made to a completion or a portion thereof.As an example, such an approach may be applied to one or more othertypes of operations (e.g., hydraulic fracturing, injecting,shutting-down, etc.).

As shown in the example of FIG. 12, the selection block 1230 may selectone or more offset wells based at least in part on one or more of anoffset well being a potential candidate 1232, as to one or morepotential “pros” (e.g., favorable outcomes) 1234, as to one or morepotential “cons” (e.g., exclusion based on an unfavorable outcomes suchas non-productive time, etc.) 1236 and/or one or more other reasons,factors, etc. 1238.

FIG. 13 shows an example of a method 1300 and an example of a system1350. As shown the method 1300 can include a reception block 1310 forreceiving digital information for multiple factors of at least one wellfor at least one corresponding well plan and for actual wellconstruction (e.g., drilling, completion, etc.) for the at least onewell, an identification block 1320 for identifying “positive” outcomesand “negative” outcomes based at least in part on the received digitalinformation (e.g., optionally via determination of differences, etc.),an analysis block 1334 for analyzing the “positive” outcomes to identifyassociated factors (e.g., via one or more techniques, which may includeone or more models and/or one or more correlation techniques), ananalysis block 1338 for analyzing the “negative” outcomes to identifyassociated factors (e.g., via one or more techniques, which may includeone or more models and/or one or more correlation techniques), and ageneration block 1340 for generating a well plan for a well thatincludes values for a plurality of factors where the values includevalues that are based at least in part on the analyses of the analysisblocks 1334 and 1338.

In the example of FIG. 13, the reception block 1310 may receive digitalinformation for a plurality of wells and for actual well construction(e.g., drilling, etc.) for at least a portion of the plurality of wells.As an example, the positive and/or negative outcomes of the block 1320may be identified and stored to a database and/or the associated factorsof the blocks 1334 and 1338 may be identified and stored to a database.As an example, information received per the reception block 1310 andinformation of the blocks 1320, 1334 and/or 1338 may be utilized forpurposes of training and/or further training one or more models. As anexample, a trained model may be a trained neural network model that canreceive one or more inputs and that can generate one or more outputs. Asan example, in FIG. 13, the generation block 1340 may generate the wellplan utilizing one or more trained models where the generated well planincludes values for various factors (e.g., trajectory, equipment,operations, etc.) that aim to increase probability of positive outcomesand/or to decrease probability of negative outcomes.

In the example of FIG. 13, the generation block 1340 can includegenerating a well plan that can reduce risks and that can increasebenefits. For example, a reduction of risk can be via the analysis ofthe analysis block 1338 (e.g., directly and/or indirectly) while anincrease of a benefit can be via the analysis of the analysis block 1334(e.g., directly and/or indirectly). As an example, a reduction of riskmay be a reduction of a risk of sticking (e.g., a drillstring gettingstuck) while an increase of a benefit may be a decrease in a time toperform an operation (e.g., an increase in rate of penetration in aparticular portion of a well trajectory to be drilled).

As an example, the method 1300 may include identifying productive timeand/or non-productive time as various types of outcomes and analyzingthose one or more types of outcomes to identify one or more associatedfactors. As an example, the generation block 1340 can include generatinga well plan that includes values (e.g., determined by a computing systemsuch as the system 1350) that aim to decrease non-productive time and/orincrease productive time (e.g., over a span of time for performance ofone or more operations for drilling a well, etc.).

In the example of FIG. 13, the generated well plan of the generationblock 1340 can include information as to trajectory 1342, bottom holeassembly 1344, operational parameters 1346 and one or more other factors1348. Such information may be in digital form and stored as a fileand/or streamed via one or more networks, cables, antennas, etc. As anexample, information may be directed to one or more destinations for oneor more purposes. For example, a trajectory team may receive informationas to trajectory 1342 while an equipment team may receive information asto the bottom hole assembly 1344 while an operations team may receiveinformation as to operational parameters 1346. In such an example, someinformation may be sent to multiple teams with different missions.

As an example, outputs from the analysis blocks 1334 and 1338 may be viaa computing system (see, e.g., the system 1350) that includes one ormore network interfaces that receive information as associated withvarious wells. As an example, such a computing system may be a wellplanning system that operates in real-time or near-real-time such that awell to be planned can be planned using the latest available informationas to a plurality of wells that have been drilled and/or that are beingdrilled.

As an example, the method 1300 of FIG. 13 can implement one or moremachine learning models as to a well, which can include models ofdrilling and/or production; noting that, as mentioned, post-drillingproduction information may be available (e.g., and/or otherpost-drilling information). As an example, a model can include nodesassociated with factors where deviation between a plan and an actual canbe utilized as a basis for identifying “positive” outcomes (e.g.,beneficial outcomes) and for identifying “negative” outcomes (e.g.,detrimental outcomes). In such an example, time may be used as a basisfor such identifications. For example, where productive time isdecreased, that may indicate a beneficial outcome; whereas, whereproductive time is increased and/or where non-productive time exists,those may indicate a detrimental outcome. While an outcome may beidentified based at least in part on a comparison of planned informationand actual information (e.g., as to one or more factors, and optionallytime), one or more underlying reasons as to the outcome may beassociated with a particular factor that is not a direct basis for adifferential between planned information and actual information. Forexample, if rate of penetration decreased below the planned rate ofpenetration over a portion of a trajectory, that difference may beidentified as a detrimental outcome (e.g., as a numeric difference,etc.); however, the underlying reason may yet to be determined. In suchan example, a model can assess the decrease in rate of penetration andrelate it to a factor or factors of a well plan such as, for example,one or more of a trajectory factor, a BHA factor, an operational factor,etc. Such a model may be a machine learning model (e.g., neural network,etc.) that associates factors of a well plan with one or more identifiedoutcomes, which can be, for example, positive and/or negative. In theaforementioned example, consider a combination of factors such asdrilling fluid and top drive factors as contributing to the decrease inrate of penetration. Where such factors are identified (e.g., andassociated values), the method 1300 may generate the well plan withvalues that aim to reduce the risk of experiencing a decrease in a rateof penetration within that portion of a trajectory (e.g., as may beassociated with a particular curve angle, a particular formation, etc.).

As an example, a machine learning model may be utilized for identifyingoutcomes, identifying factors and, for example, generating a well planbased on input information for a well to be drilled according to thewell plan (e.g., preliminary specification of location, preliminaryspecification of equipment, etc.). As an example, preliminaryspecification of equipment can include preliminary specifications of oneor more BHA components (e.g., drill bit, stabilizers,logging-while-drilling, etc.). As an example, a generated well plan mayoutput different and/or additional specifications for such equipment.

In the example of FIG. 13, the generated well plan may be output by theblock 1340 with information as to how much time may be saved by drillingthe well according to the generated well plan. In such an example, timesmay be plotted versus length for the generated well plan. As an example,a graphical user interface may render information to a display thatincludes information for offset wells (e.g., as associated with thereception block 1310, etc.) and information for the generated well plan.In such an example, an operator may, a priori, understand where and howa well plan is improved upon, similar too, etc., one or more other wellplans and/or actual drilled wells.

As an example, the method 1300 can be utilized in a manner that chainswell information as wells are being planned and drilled. For example,where ten wells have been drilled, the generated well plan of thegeneration block 1340 may be for an eleventh well. As an example, themethod 1300 may be implemented for wells that are actively beingdrilled. For example, an offset well may be a well that is several daysahead in being drilled compared to a well for which a well plan isgenerated. In such an example, the several days of information can bereceived and analyzed for differences as to that well between plannedand actual drilling where such differences are assessed and furtheranalyzed for generation of the well plan for the well that is to bedrilled or that is actually being drilled, but at a stage that is behindin time and/or depth (e.g., length, etc.).

As an example, the method 1300 of FIG. 13 may include computingresiduals for factors of a well plan with respect to actual factors fromdrilling at least a portion of the well based at least in part on thewell plan.

As an example, the method 1300 of FIG. 13 may include outputting lessonslearned through a comparison of well plans to actual drillinginformation. As an example, the method 1300 of FIG. 13 can includeidentifying and outputting factors that may be consistently having largedifferentials between planned and drilled. For example, where a rate ofpenetration (e.g., as an operational parameter) as planned through aparticular formation (e.g., lithology) is consistently large in onedirection (e.g., negative or positive), that factor may be flagged forassessment, which may include “offline” human assessment. As an example,where a portion of a trajectory through a particular formation (e.g.,lithology) is consistently being altered as to angle by a driller thatmay have drilled a plurality of wells through that formation, thatportion of the trajectory (e.g., as a trajectory factor) may be flaggedfor assessment.

As an example, the method 1300 of FIG. 13 may include ranking of theidentified outcomes where various outcomes may be considered and othernot considered. For example, if a negative outcome ranks number six often, the method 1300 may skip negative outcomes six to ten to focus onnegative outcomes one to five. As an example, where a negative outcomeis a “reality” in that a well plan was inaccurate as to being overlyoptimistic, etc., that negative outcome may optionally be ignored. Forexample, a computing system may allow for a user to select one or moreidentified outcomes for purposes of generating a well plan. In such anexample, the computing system may render information to a display in theform of a graphical user interface where a user can review positive andnegative outcomes and select from those outcomes which ones are to beutilized in generating a well plan per the block 1340.

As an example, a well plan can be or include a series of cascadingdecisions. As an example, a well plan may be implemented at least inpart as a series of cascading decisions. In such an example, apreliminary decision may pertain to instrumentation of a drillstring, arig, etc. for acquisition of information germane to planning (e.g., acomputing system that can implement a machine learning planningtechnique or techniques, etc.).

As to the example system 1350, as shown in FIG. 13, it can includecomputing resources and one or more associated interfaces 1352 (e.g.,for transmission and/or reception of information), a search engine 1353that can search for one or more offset wells for which information maybe stored in a database, one or more models 1354, one or more trainingalgorithms 1355 that can train a machine model (e.g., neural network,etc.) to generate a trained machine model, one or more correlationalgorithms 1356, one or more generation algorithms 1357 and one or moreother features 1358.

In the example of FIG. 13, the search engine 1353 may operate in anautomated mode, a semi-automated mode and/or a manual mode. As anexample, one or more search criteria may be received by the searchengine 1353 in one or more manners (e.g., via parsing of receivedinformation for a well, via one or more fields of a GUI, etc.). As anexample, a proximity criterion may be a radius from a well for which awell plan is to be generated per the block 1357. In such an example,offset wells that are within the radius may be selected. As an example,training of a model or models may occur based on information for aplurality of wells. In such an example, as wells are completed (e.g.,drilled, cased, producing, etc.), information for such wells may beutilized for further training. As to correlation, as mentioned, one ormore correlation techniques may be implemented to associate factors suchthat an outcome, which may be a factor (e.g., a primary factor, etc.)can be associated with one or more other factors. In such an example,values for one or more factors may be utilized for purposes of well plangeneration, well plan revision, control of one or more field operations,etc.

As shown in FIG. 13, as an example, the system 1350 may be utilized toimplement at least a portion of the method 1300. For example, aninterface of the system 1350 can provide for receiving digitalinformation per the reception block 1310, computing resources of thesystem 1350 can provide for determining differences between factors ofwell plans, computing resources of the system 1350 can provide foridentifying various factors (e.g., primary, secondary, tertiary, etc.)based on one or more differences (e.g., and optionally magnitude of suchdifferences, occurrence of such differences with respect to time, withrespect to distance, etc.), and computing resources of the system 1350can provide for training one or more models (e.g., one or more neuralnetwork models, etc.) to generate one or more trained models. In such anexample, input information may be received for a well to be plannedwhere such information may be directly input and/or pre-processed forinput to a trained model or trained models for purposes of generatingoutput that can be utilized for generating a digital well plan.

As mentioned, as an example, a trained model may be utilized during anoperation such as a drilling operation performed by a rig at a rigsiteas specified by a digital well plan to receive information as to adeviation from the digital well plan to output an outcome based on thedeviation. As an example, consider deviation information pertaining todogleg severity (DLS) of a borehole being drilled by the drillingoperation where the deviation information is received by a trainedneural network model that outputs a likely outcome or a plurality ofpossible outcomes with associated likelihoods of occurrence. In such anexample, a notification can be issued to equipment at a rigsite that canaddress one or more of the outcomes. For example, a notification maypertain to one or more operations (e.g., drilling, casing, cementing,etc.) that may act to reduce the risk of an outcome and/or to reduce theimpact of an outcome.

As to a dogleg, it can be defined as a particularly crooked or curvedplace in a bore where the trajectory of the bore in three-dimensionalspace changes relatively rapidly. While a dogleg can be createdintentionally as part of a digital well plan, for example, to beperformed by directional drilling, a dogleg may refer to a section of aborehole that changes direction faster than expected or desired, whichmay be accompanied by detrimental effects that may manifest short-term,medium-term and/or long-term. As to some examples, short-term may impactan ongoing drilling operation, medium-term may impact a completionsoperation, and long-term may impact production from the borehole as acompleted well. As an example, an outcome may be associated with atime-frame or time-frames as to which the outcome may be expected.

In surveying wellbore trajectories, a standard calculation of doglegseverity may be made, for example, expressed in two-dimensional degreesper 30 meters (e.g., or 100 feet) of borehole length. As an example,where a dogleg in a section of a digital well plan deviates from one ormore specifications of the digital well plan, it is possible that aplanned casing string may no longer easily fit through that section. Asan example, where a dogleg in a section of a digital well plan deviatesfrom one or more specifications of the digital well plan, there may berepeated abrasion by the drillstring in a particular location of thedogleg, which may result in a worn spot called a keyseat, in which thebottom hole assembly components may become stuck as they are pulledthrough the section. As an example, where a dogleg in a section of adigital well plan deviates from one or more specifications of thedigital well plan, casing cemented through the dogleg may wear at a ratethat is quicker than planned due to higher contact forces between thedrillstring and the inner diameter (ID) of the casing through thedogleg. As an example, where a dogleg in a section of a digital wellplan deviates from one or more specifications of the digital well plan,a relatively stiff bottom hole assembly may not readily fit through thedogleg section as may have been drilled with a relatively limber bottomhole assembly. As an example, where a dogleg in a section of a digitalwell plan deviates from one or more specifications of the digital wellplan, an excessive dogleg increase may increase overall friction to adrillstring, increasing the likelihood of getting stuck or not reachingthe planned target and/or total depth. As an example, a recommendationmay accompany an outcome notification, which may include one or moreactions that can be taken to address short, medium and/or long termeffects. As an example, a recommendation may include taking remedialaction, such as, for example, reaming or underreaming through thedogleg, or even sidetracking in extreme situations.

As an example, an ANN can be trained as to deviations from digital wellplans (e.g., deltas between planned and actual) as to doglegs withrespect to outcomes for a plurality of drilled boreholes that caninclude boreholes that have been completed as wells and can includewells that have been utilized for producing fluid from one or morereservoirs. As an example, a trained ANN can be utilized to outputoutcomes given a deviation as to a dogleg from a dogleg of a digitalwell plan during a drilling operation.

As an example, a modeling system can learn (e.g., progressively improveperformance) to do tasks by considering examples, which may occuroptionally without task-specific programming. As an example, a trainingset for a modeling system that includes one or more models to be trainedcan include information for a plurality of wells where each of the wellsmay have been drilled at least in part according to a plan. As anexample, a training set may prepared by analyzing data for purposes ofdetermining differences for a variety of factors and/or associating avariety of factors.

As an example, an ANN can be based on a collection of connected unitscalled artificial neurons where a connection between neurons cantransmit a signal to another neuron (or neurons). As an example, areceiving neuron can process the signal(s) and then, for example, signaldownstream neurons connected thereto. As an example, an ANN may includelayers where an input layer (e.g., for deltas) and an output layer(e.g., for outcomes) are exposed while other layers may be hidden. As anexample, neurons of an ANN may have state, generally represented by realnumbers, which may be, for example, between 0 and 1, −1 and 0, −1 and+1, etc. As an example, neurons and synapses may also have a weight thatvaries as learning proceeds, which can increase or decrease the strengthof a signal that it may send downstream. As an example, a threshold orthresholds may be utilized such that if an aggregate signal is below (orabove) that level, a downstream signal is sent.

As mentioned, neurons can be organized in layers where, for example,different layers may perform different kinds of transformations on theirinputs. As an example, signals can travel from the first (input), to thelast (output) layer, possibly after traversing one or more layers one ormore times. As mentioned, as an example, input(s) can be informationabout a well to be drilled and output(s) can be one or more factors thatcan be directly and/or indirectly specified in a digital well plan fordrilling the well. As mentioned, input(s) can be information about awell being drilled and output(s) can be outcome(s) based on deviation(s)in one or more factors that can be directly and/or indirectly specifiedin a digital well plan for drilling the well.

FIG. 14 shows an example of a method 1400 that includes a drill block1410 for drilling a well according to a well plan for drilling the well(e.g., a digital well plan), a reception block 1420 for receivinginformation for the well (e.g., during the drilling of the well), ananalysis block 1430 for analyzing at least a portion of the receivedinformation with respect to at least a portion of the information of thewell plan, a decision block 1440 for deciding whether an unexpectedcircumstance exists based at least in part on the analyzing, an analysisblock 1450 for analyzing the unexpected circumstance as to being apositive outcome or a negative outcome (e.g., or a neutral outcome) andan optional revision block 1460 for optionally revising the well planbased at least in part on the analyzing of the unexpected circumstance.The method 1400 can include storing information generated via the one ormore analyzes of blocks 1430 and 1450, which may be utilized forpurposes of well planning. For example, consider an analysis systemblock 1435 and a database block 1455, which may be operatively coupledand include accessible data associated with outcomes and/or one or moremachine models that are trained and/or that can be trained and used fordetermining an outcome or outcomes based at least in part on informationfor a well (e.g., well plan information and actual information fromdrilling of the well). As an example, the method 1400 can includestoring information received per the reception block 1420 and/or storinginformation as to one or more unexpected circumstances per the decisionblock 1440. Various types of information may be stored as part of themethod 1400, which may be utilized, for example, to plan, revise a plan,control an operation, etc.

As to the revision block 1460, where an outcome does not reasonableinform a future operation as to drilling of the well, the analyzing ofthe block 1450 may, for example, ignore, store, etc. the reason orreasons for the outcome.

As an example, the method 1400 of FIG. 14 can include issuing one ormore signals, instructions, commands, etc., based at least in part on ananalysis and/or a decision (e.g., the block 1430, the block 1440, theblock 1450, etc.). In such an example, such a signal, instruction,command, etc. may be transmitted via an interface of a computing system(e.g., via a network interface) and, for example, may be received by oneor more pieces of equipment (e.g., a control interface of a controller,an actuator, etc.).

As an example, a method can include instrumenting a system such thatinformation may be acquired. As an example, a method can includedeciding that drilling faster or drilling slower than expected can beunexpected circumstances. In such an example, the system (e.g., drillingsystem) may be instrumented with sufficient sensors, etc. to be able toacquire information that may be analyzed to determine one or moreunderlying reasons as to why drilling is at an unexpected rate (e.g.,whether faster or slower). As an example, information may be lengthversus time information such that a rate of penetration can bedetermined. As an example, information as to drilling speed (e.g.,rotational speed), drilling fluid flow rate(s), drilling fluid density,etc., may be acquired via instrumentation that may allow for an analysisto determine which one or more of such factors may have attributed to anunexpected rate of penetration. In such an example, a model may beutilized, which may be a machine learning model of drilling.

As an example, the method 1400 of FIG. 14 can include capturing stateinformation as to a state of a drilling rig, as to state of adrillstring, as to state of a formation at a time associated with anunexpected circumstance. As an example, states may be captured for timesprior to occurrence of the unexpected circumstance, which may beanalyzed as to one or more underlying reasons therefor. As an example, amodel may be a state-based model that can be implemented in a methodthat includes receiving state information for a time or a plurality oftimes and analyzing the state information for an underlying cause of anunexpected circumstance or unexpected circumstances. As an example, themethod 1400 of FIG. 14 can include a counter that counts a number oftimes a particular type of unexpected circumstance has occurred. As anexample, the method 1400 of FIG. 14 may identify in the well plan one ormore times, depths, lengths, etc. where an unexpected circumstance mayagain occur, for example, if a particular revision to the well plan isnot made.

As explained with respect to the method 1400 of FIG. 14, unexpectedcircumstances can be determined based on one or more difference betweena well plan's actions (e.g., decisions in a series, etc.) and actualactions (e.g., decision in a series, etc.). Such a method may, inreal-time, provide for an assessment of a current well plan andoptionally revision of the well along with, for example, output of whatmight happen if one or more revisions are made to the well plan and/orif one or more revisions are not made to the well plan.

As an example, a positive outcome of a drilling operation may beachieving a rate of penetration (ROP) that is greater than expected pera well plan. In such an example, where information associated with a BHAis a factor or factors along with weight on bit (WOB), such a factor orfactors may be identified via analysis as underlying causes of achievingthe unexpected ROP. In such an example, the ROP may pertain to aparticular formation, a particular depth, etc., such that one or morelessons learned (e.g., by a machine learning system) may be applied toone or more other wells to be planned that include drilling into thatformation at that particular depth, etc. Where a favorable outcome isdesired, the learning of the offset well (as discussed above) may beutilized. In the foregoing example, where an analysis indicates that BHAis a causal factor but not WOB, as to the favorable ROP, such a learningmay be utilized and associated with one or more pieces of the BHA and/oroperation thereof. Further, where BHA is not a causal factor but WOB isa causal factor, then WOB may be associated with a formation, a rigsystem, etc.

As an example, the method 1400 of FIG. 14 can be a dynamic method thatmay be implemented in multiple instances for a plurality of wells thatare being drilled. In such an example, the multiple instances may belinked, for example, via a computing system, which may be, for example,at least in part a cloud-based computing system. In such an example, anInternet-of-Things (IoT) approach may be made to instrumentation in asecure manner for one or more rigsites in a field or fields.

As an example, a method can include outputting a heat map as to variousfactors and/or outcomes as to one or more drilling operations, whetherongoing, past or planned.

As an example, a method can include capturing information when adeviation has occurred or may occur. As an example, a heat map mayextend to future times for a well plan, which may indicate where one ormore deviations may occur from a well plan. Such a heat map may indicatesensitivities of a well plan to actual or possible deviations. As anexample, a heat map can include positive indications and/or negativeindications. In such an example, a heat map or heat maps may be factorspecific in a manner where a red color (e.g., high heat) indicates thata factor is more likely to deviate in a detrimental manner from itsplanned factor value and where a blue color (e.g., low heat) indicatesthat a factor is more likely to deviate in a beneficial manner form itsplanned factor value. As to factors that are less likely to deviate,these may be rendered to a display in a heat map with a neutral color(e.g., white, gray, etc.). In such an example, a heat map may be afactor map with respect to time, with respect to depth, with respect tolength, or with respect to one or more other variables.

As an example, a GUI such as the GUI 1700 of FIG. 17 may be color-codedwith respect to outcomes output by a computational framework that canreceive input such as deltas between planned and actual actions. Forexample, various activities and/or sub-activities may be color-codedbased at least in part on one or more outcomes output by such acomputational framework (see, e.g., the system 1350 of FIG. 13, theanalysis system 1435 and the database 1455 of FIG. 14, etc.).

As an example, consider a drill bit with an expected ROP where downholefactors can be ascertained. In such an example, a method can includeascertaining whether the expected ROP can be achieved for one or moreportions of a trajectory of a well to be drilled. Such a method may bedynamic and be updated during drilling of the well where the method canidentify positive outcomes and identify negative outcomes based at leastin part on data acquired during drilling.

As an example, a method can include accessing a database that includesinformation for more than approximately 100 wells, more thanapproximately 500 wells, more than approximately 1000 wells, etc. Insuch an example, for a single well, a portion of the other wells can beoffset wells, which may be selected based on one or more selectioncriteria (e.g., location, type of equipment, etc.). As an example, afactor may be represented as a dimension of a well plan. For example, amodel that can represent a drilling process can include dimensions whereat least some of the dimensions represent factors.

As an example, a drilling operations system can receive and implement adigital well plan where the digital well plan is relied upon todetermine a series of cascading decisions where each of the decisionscan be a “test case” where a difference may exist between the digitalwell plan and actual implementation thereof (e.g., via execution of adecision by the drilling operations system). As mentioned, a differencemay be positive or negative with respect to it being beneficial ordetrimental.

As an example, a well plan as executed can be discretized into timeand/or distance based increments that can be individually classified asbeing positive, neutral or negative. As an example, a graphical userinterface may allow for rendering a visualization to a display as topositive outcomes, negative outcomes, etc., with respect to one or morevariables (e.g., time, depth, length, etc.).

As an example, increments may optionally be determined based on type ofactivity and/or type of equipment (e.g., data acquisition capabilities).As an example, a method may be implemented in a manner that candynamically request a change (e.g., an adjustment, etc.) in a dataacquisition rate. For example, where a positive and/or a negativeoutcome may be uncovered and/or predicted, the method 1400 of FIG. 14may include issuing an instruction (e.g., a signal, a command, etc.)that causes equipment in the field to increase and/or decrease a dataacquisition rate. In such an example, the increased acquisition rate(e.g., increased frequency of data acquisition) may provide for a moredetailed analysis as to one or more underlying causes of an outcome.

As an example, a digital well plan can include a data acquisitionschedule for one or more pieces of data acquisition equipment (e.g., oneor more sensors, etc.). Such a schedule or schedules may be a factor orfactors that are part of the digital well plan that may be amenable torevision, etc. As an example, a machine learning model may optionallyinclude features for modeling data sufficiency where if data sufficiencycan be linked to a data acquisition rate. In such an example, themachine learning model may output information for one or more dataacquisition rates of one or more pieces of equipment, which may dependon one or more factors (e.g., activity, depth, length, time, etc.). Asan example, a data acquisition rate may be set in a digital well plan ina logical manner. For example, if the drilling fluid flow rate exceedsX, then increase the data acquisition rate of sensor Y to a rate that isgreater than Z.

As an example, consider vibration data as an example of a type of data.In such an example, a digital well plan may specify logic that can beimplemented to control the acquisition rate of vibration data by one ormore vibration sensors. In such an example, where vibration increasesabove a threshold, the acquisition rate may increase for acquisition ofvibration data. As an example, a method may include predicting a certaininteraction for a BHA during drilling (e.g., based on BHA type,operational parameters, depth, length, formation, mud flow rate, muddensity, etc.). In such an example, the method may aim to makerecommendations and/or revisions responsive to actual interaction wherevibration data may be helpful for purposes of understanding theinteraction, controlling the drilling process, revising a well plan,etc. In such an example, the method may implement a more advancedvibrational analysis technique (e.g., as to spectral distribution, trendof vibration, etc.).

As an example, where during drilling, pressure in a drilling fluidsystem is fluctuating (e.g., per acquisition of pressure data at a firstacquisition rate), a digital well plan may include logic that can beimplemented by a drilling system to increase acquisition rate of one ormore types of data such as, for example, RPM, WOB and pressure (e.g., toa higher second rate). Such an approach to determine a correlationbetween the fluctuating and one or more factors. As an example, acorrelation may be model based where, for example, a model may accountfor one or more aspects of a drilling operation (e.g., backpressure maybe determined in part by an amount clearance around a drill bit of a BHAas to its fluid nozzles with respect to a formation, etc.). As anexample, information about a BHA may indicate that it includes a drillbit and a reamer, where such factors can be related to pressurefluctuations. In such an example, logic of a well plan may call forincreased acquisition of annulus pressure and/or standpipe pressure, tohelp assess and/or control the scenario (e.g., fluctuations of pressure,etc.). Such information may lead to one or more lessons learned, whichmay inform further drilling of the well and/or drilling of one or moreother wells.

As an example, logic of a well plan can include data acquisition logicas to primary parameters (e.g., primary factors) and other factors(e.g., secondary and/or tertiary factors). In such an example, thefactors may be associated via a model, which may be a machine learningmodel. As an example, a model may be implemented in real-time during adrilling operation where a revision to a well plan may be made thatincludes revision one or more logics associated with data acquisition.In such an example, the model as part of a computing system candetermine what types of data and/or data acquisition rate(s) that canhelp to inform the model for purposes of a well currently being drilledand/or for planning of one or more other wells (e.g., not yet drilledand/or partially drilled).

As an example, consider an expected rate of penetration of approximately15 meters per hour and an actual rate of penetration of approximately 25meters per hour. Such an occurrence can be classified as a positiveoutcome. A drilling system that includes or is operatively coupled to awell planning system, may analyze the ROP as a primary variable inconjunction with one or more other variables, which may be at least inpart a cause of the positive outcome. In such an example, the system orsystems may optionally operate in real time. Such a system or systemsprovide for cross-coupling of factors.

As an example, a digital well plan can be a revisable well plan, whichmay include logic as to data acquisition that may optionally berevisable. As an example, a digital well plan may be received at leastin part by a drilling system for purposes of execution of at least aportion thereof, which may be executed as a series of decisions that aremade as to equipment at a rigsite.

As an example, a portion of a digital well plan may be renderable in ahard copy version, for example, in paper as may be utilized by anoperator or operators at a rigsite (e.g., in a doghouse, etc.).

As an example, a portion of a digital well plan may be pushed to one ormore sub-systems. For example, consider a portion of a digital well planthat includes one or more settings for one or more pieces of equipmentof a subsystem. In such an example, a subsystem may pertain to drillingfluid, data acquisition, weight on bit, a top drive, latching, etc.

As an example, a plan may include information as to one or morecompletions. As an example, a plan may include information as to one ormore stages of hydraulic fracturing. As an example, a plan may includeinformation as to one or more types of treatments.

As an example, a plan may include information as to one or more tests.For example, consider information as to pressure testing a tubing,pressure testing a packer in an annulus, etc.

As an example, a computing system that provides for well planning caninclude a simulator that can simulate time, simulate depth, simulatelength, etc. Such a computing system may simulate multiple variables andassociated such variables with particular operations (e.g., decisions,etc.).

As an example, a computing system that provides for drilling operations,etc., can include one or more times, clocks, etc. that can record timeas associated with one or more operations, one or more conditions, etc.As an example, a computing system that provides for drilling operationscan include input(s) for sensor(s) or other information as to depthand/or length of a borehole in a formation and/or optionally as toparticular equipment, fluid, etc. in the borehole. As an example,information compared as to a well plan and actual drilling may extendbeyond time. As an example, an analysis may include time and depthand/or length to determine an outcome or outcomes and/or underlyingcause or causes thereof. As an example, an activity time for aparticular operation may be associated with a start time and a finishtime as well as one or more depths and/or lengths. In such an example,cross-correlating with one or more factors may occur with respect to oneor more of time and/or depth and/or length. In such an example, time mayallow for one type of cross-correlation while depth and/or length mayallow for another type of cross-correlation. Such cross-correlation(s)may be for a primary factor with respect to one or more other factorswhere, for example, the primary factor may be classified as beingindicative of a positive/beneficial outcome or a negative/detrimentaloutcome.

As an example, one or more of the methods of FIGS. 10, 11, 12, 13 and 14may be implemented at least in part by one or more of the systems ofFIGS. 2, 3, 4, 5, 6, 7, 8, 9, 13 and 14.

Various tools may be implemented to perform a method or methods, whichmay be in the form of a workflow or workflows. For example, a tool canbe a graphical user interface (GUI) that is rendered to a display wherea human input device (HID) can generate signals, instructions, etc.,responsive to human input. An HID may be operatively coupled to one ormore devices and/or systems. As an example, an HID may be a touchscreen,a mouse, a trackpad, a roller, a stylus, a virtual reality (VR) system,etc.

FIG. 15 shows an example of a graphical user interface (GUI) 1500 thatmay be rendered to a display as well as a graphic 1510 that illustratesvarious terms that may be utilized to describe a trajectory, a borehole,a well, a wellbore, etc. As an example, one or more factors of atrajectory may correspond to one or more of the terms that are shown inthe graphic 1510 (see also, e.g., the holes 272, 274, 276 and 278 ofFIG. 2).

As an example, a deviation from a digital well plan may be discerned bycomparing a traveling plate view of the digital well plan to a travelingplate view of a well (e.g., borehole) as actually drilled. In such anexample, the deviation may be input to a system that can output anoutcome or outcomes. In such an example, an outcome may includeinformation that can be represented in a traveling plate view, which mayshow a beneficial outcome (e.g., positive), a neutral outcome and/or adetrimental outcome (e.g., negative). For example, consider a beneficialoutcome as wells being adequately spaced in a manner that may exceedspacing expected by a digital well plan; whereas, a detrimental outcomemay be spacing less than expected by a digital well plan.

In the example of FIG. 15, the GUI 1500 can illustrate various wellssuch as a subject well and one or more offset wells. As shown, atraveling plate representation may be utilized for one or more of thewells. The traveling plate may be a planar representation with aboundary or circumference where a trajectory of a bore (e.g., awellbore) may be within the boundary or circumference. As an example, atraveling plate may travel along a trajectory where the plate is asubstantially planar representation that can maintain orthogonality tothe trajectory, for example, a portion of the bore above and a portionof the bore below can be substantially normal to the plate (e.g., one an“upward” normal and the other a “downward” normal, which may be normalvectors).

As an example, at a particular length along a trajectory a view may berendered such as one or more of the views to the left of the travelingplate view. In such an example, various types of information may berendered in these views. As an example, a graphic can represent anotherwell or graphics can represent other wells (e.g., bores or wellbores).As an example, as shown in FIG. 15, the graphics include no-go zones orno-go regions, which when circular may be referred to as no-go circles.These zones or regions can represent areas that are to be avoided by aselected subject well being planned, for example, to reduce risk ofcollision between trajectories.

FIG. 16 shows an example of a graphical user interface (GUI) 1600 thatincludes a representation of a well that can be, for example, an actualwell or a planned well. As shown, various factors exist that candescribe a well. Such factors can include, for example, negativesection, positive section, sail angle, hole section and associated size,nudge section, kick-off section, tangent section, build section, andreservoir entry. As an example, a digital well plan can include one ormore of such factors where a delta may be determined based on actualdata from drilling a well. For example, consider a sail angle beingdifferent between the digital well plan and the actual sail angle of aportion of a well being drilled.

FIG. 17 shows an example of a graphical user interface (GUI) 1700 thatincludes various subsystem tasks as may be part of a well plan. Forexample, a rig up subsystem, a casing subsystem, a cement subsystem, adrilling subsystem and a rig down subsystem are illustrated as somepossible examples of subsystems that can include associated tasks. Asshown in the example of FIG. 17, the GUI 1700 includes a timeline, whichcan be incremented by minute, hour, day, etc. In the example of FIG. 17,the GUI 1700 can be render information as to scheduled tasks that areorganized by subsystem type where a scheduled task may aim to achieve adesired state of wellsite equipment. As an example, the GUI 1700 mayoptionally be operatively coupled to the output and/or input of one ormore of the methods 1000, 1100, 1200, 1300 and 1400 of FIGS. 10, 11, 12,13 and 14.

In the example of FIG. 17, the various tasks are shown as Sub-Activitiesand as other types of tasks (e.g., Idle, Bit Run, etc.), which may beconsidered to be Sub-Activities. As an example, graphical controls canallow for addition of one or more new activities (e.g., scheduling ofnew tasks). As an example, graphical controls can allow for reschedulingone or more tasks.

As an example, the GUI 1700 can include control graphics for optionsanalyses as to implementation options as to one or more tasks. Forexample, a user may touch a touchscreen display as to the Sub-Activity 2graphic under the Casing subsystem heading and call for selection of animplementation option.

In the example of FIG. 17, a dashed box represents a display device ontowhich the GUI 1700 can be rendered. For example, consider a flat paneldisplay, which may be, for example, a touchscreen display.

As an example, one or more features illustrated in FIG. 17 may be linkedto trajectory information and, for example, optionally update, revised,etc., in response to nudging of a portion of a trajectory, etc.

As shown in FIG. 17, a well plan can include information as to variousactivities, some of which may be classified as Sub-Activities as shown.Examples in FIG. 17 include activities such as rig up, casing, cement,drilling, and rig down. Such activities are shown with respect to a timeline, which is indicated in month-date format (e.g., 12/11, 12/12,12/13, 12/14, 12/15). As shown in FIG. 17, various activities may beperformed sequentially and/or in parallel.

The GUI 1700 of FIG. 17 shows an example of a graphical notice renderedto a display as may be part of the GUI 1700. In the example, thegraphical notice indicates that a deviation from a digital well plan hasoccurred. For example, where Bit Run 1 deviates from one or more aspectsof the digital well plan, a computation framework can issue a signal tocause the graphical notice to be generated and rendered to a display,for example, in close proximity to a particular action. As an example,such a notice may cause or call for creation of one or more newactivities. For example, where the Bit Run 1 deviated from the digitalwell plan, an existing Bit Run may be adjusted and/or a new Bit Run maybe created. In such an example, the adjustment and/or creation may bebased on output from a computational framework that operates at least inpart on the deviation, which can be a delta. For example, the deviationmay be input to a trained machine model to generate output thatindicates whether the deviation (e.g., delta) is likely to cause adetrimental, beneficial or neutral outcome. The output may be renderedto a display, for example, as part of the GUI 1700 to provideinformation as to how or whether to adjust and/or create a Bit Run. Asan example, a traffic light graphic may be rendered as a graphic orgraphical control to a display where, upon occurrence of a deviation(e.g., a delta), the traffic light graphic renders a green light for abeneficial outcome, a yellow light for a neutral outcome and a red lightfor a detrimental outcome. In the example of FIG. 17, a traffic lightgraphic is shown within a window of the Bit Run 1, with a “red light”highlighted. As indicated, such a notification may lead to an adjustmentand/or a creation of a task or tasks (see, e.g., adjust as to Bit Run 2and/or create as to creation of a new Idle or Bit Run. As mentioned, aheat map approach may be implemented that color-codes various activitiesbased at least in part on one or more deviations (e.g., deltas) and oneor more associated outcomes from a system.

FIG. 18 shows an example of a GUI 1800 that is rendered to a displaydevice 1801, represented by a dashed box. For example, consider a flatpanel display, which may be, for example, a touchscreen display.

In the example of FIG. 18, the GUI 1800 may be an operational dashboardwhere the state of one or more pieces of equipment, operations, etc. maybe rendered visually, for example, via graphics and/or numbers. As anexample, various colors may be utilized to convey state information. Asan example, audio may be associated with the GUI 1800 and changesthereto, etc. For example, where a parameter reaches a limit, a colorchange may occur to a graphic of the display device 1801 and an audioalarm may be rendered via one or more speakers.

As an example, the GUI 1800 may optionally be operatively coupled to theoutput and/or input of one or more of the methods 1000, 1100, 1200, 1300and 1400 of FIGS. 10, 11, 12, 13, and 14.

As shown in FIG. 18, the GUI 1800 can include one or more types ofnotification graphics or graphical controls such as, for example, atraffic light control as shown with respect to rate of penetration at aparticular time and as to a notification as shown with respect tosurface torque. In both instances, the notifications can be based on adeviation from a digital well plan (e.g., a delta). As shown, anotification can include information based on outcome for the deviation(e.g., an outcome for a delta). As mentioned, a delta can be detectedduring drilling where the delta is input to a trained machine modelcomputational framework to determine a likely outcome where the likelyoutcome may be detrimental, beneficial or neutral. As mentioned, anoutcome may be determined from a lookup (e.g., a search) of a databasewhere such a search can be a query based on the deviation (e.g., delta)that looks for a matching deviation (e.g., delta) that has acorresponding outcome that can be returned as a search result.Conveyance of such information during drilling can improve drilling, forexample, to achieve a desired rate of penetration, which may depend on,for example, an amount of surface torque (e.g., or weight on bit, RPM,flow rate, etc.).

FIG. 19 shows an example of a GUI 1910, which is shown as a table ofdata. As an example, the values (e.g., data) in the GUI 1910 may beselectable and editable and, for example, determined by a well planningsystem and/or revised by a well planning system. As an example, the GUI1910 may optionally be operatively coupled to the output and/or input ofone or more of the methods 1000, 1100, 1200, 1300 and 1400 of FIGS. 10,11, 12, 13, and 14. As an example, the data in the table of the GUI 1910of FIG. 19 may be data accessed from a digital well plan. For example,consider a digital well plan that is accessed by a computationalframework that can be utilized during drilling or other rigsiteactivities. In such an example, the computational framework can accessthe digital well plan and render information from the digital well planto a display, for example, via a GUI. As an example, such informationcan include data that can be utilized to determine whether a deviationhas occurred with respect to the digital well plan. For example,consider the measured depth (MD) entry at 3284.31 feet does not have aninclination angle of 6.92 degrees. In such an example, the deviation canbe in a number of degrees at a particular depth, which may be associatedwith other information as in the table. The number of degrees deviationand optionally other information can be input to a trained machine modelto generate output such as a likely outcome of the deviation from theindicated inclination angle at the particular measured depth (MD). Inthe foregoing example, the deviation in inclination angle at themeasured depth (MD) may give rise to a dogleg severity (DLS) issue,which is specified in degrees per 100 feet in the table of the GUI 1910,which itself may be determined and give rise to another deviation ordelta that can be processed via a trained machine model and/or via alookup in a database. As mentioned, detection of such a delta can causea computational framework to issue a notification such as a graphic orgraphics to a display (e.g., via a GUI or GUIs, etc.).

As an example, a wellbore trajectory can be a geometric trace thatconnects a well surface and one or more drilling targets. As an example,when a wellbore trajectory is designed by a drilling engineer, variousrules (e.g., or Performance Indexes, abbreviated PIs) that a trajectorymay expect to follow, for example, its kickoff point is to be below themudline, it is not to have a collision issue with another existing wellor wells, and its dogleg severity (DLS) is to be within the ability thatdrilling tools can turn, etc.

As an example, various constraints may be imposed on a method that candesign one or more trajectories. For example, consider one or more ofthe following types of constraints: a landing direction constraint, aleadline constraint, a hardline constraint, a hole size constraint, acasing point constraint, a tool capability of a BHA constraint, aformation characteristic constraint, a bore type constraint, a number oftargets constraint, a type of targets constraint, an anti-collisionconstraint, etc.

As to a bore constraint, consider one or more of a main bore constraint,a lateral bore constraint, and a re-planning bore constraint. As tomultiple targets, one or more particular constrains may consider arelationship or relationships between the targets. As an example, aconstraint may be imposed as to one or more local practices and/orclient preferences.

As an example, a method can include receiving a digital well plan;issuing drilling instructions for drilling a well based at least in parton the digital well plan; comparing acquired information associated withdrilling of the well with well plan information of the digital wellplan; and outputting results based at least in part on the comparing ofthe acquired information with the well plan information. In such anexample, the method can include determining a difference between theacquired information and the well plan information. Further, forexample, consider identifying the difference as being associated with apositive outcome, identifying the difference as being associated with anegative outcome and/or analyzing the difference to determine at leastone factor of the digital well plan as being at least in part anunderlying cause of the difference. As an example, consider a differencein a rate of penetration of the drilling or another type of differencesuch as, for example, a difference in a dogleg of a wellbore of a well.As an example, a difference can be a deviation between a digital wellplan operation and an actual operation, between a digital well planpiece of equipment and an actual piece of equipment, between acharacteristic of a digital well plan bore and an actual bore, etc. Asan example, a difference may be referred to as a delta. As an example,such a delta may be utilized for determining whether the delta is likelyto give rise to an outcome or outcomes. As an example, a notificationmay be issued and rendered to a display of a graphical user interface(GUI) that pertains to an outcome or outcomes.

As an example, a method can include comparing that includes inputting adifference to a trained machine model that generates results, where theresults include at least one outcome associated with the difference.

As an example, a method can include comparing that includes performing asearch of a database utilizing the difference as a query wherein thesearch generates results, where the results include at least one outcomeassociated with the difference.

As an example, a method can include analyzing a difference to identifyat least one factor specified in the digital well plan as beingassociated with the difference. For example, consider situations wherethe at least one factor includes an equipment factor, includes anoperational factor and/or includes a formation factor.

As an example, a method can include storing results to a database, whichmay be utilized, for example, to generate and/or revise a well plan.

As an example, a method can include generating a digital well plan for awell. For example, consider receiving information for the well,selecting one or more offset wells, accessing comparison results for theone or more offset wells, and generating the digital well plan based atleast in part on at least a portion of the comparison results for theone or more offset wells.

As an example, a method can include revising a digital well plan for awell. For example, consider revising the digital well plan based atleast in part on at least a portion of comparison results for the well.As an example, revising a digital well plan can be based at least inpart on comparison results for one or more offset wells. In such anexample, at least one of the one or more offset wells may be beingdrilled simultaneous to the well for which the well plan is beingrevised.

As an example, a digital well plan can include one or more of atrajectory factor, a bottom hole assembly factor, and an operationalfactor.

As an example, a system can include one or more processors; memoryoperatively coupled to the one or more processors; andprocessor-executable instructions stored in the memory and executable byat least one of the one or more processors to instruct the system to:receive a digital well plan; issue drilling instructions for drilling awell based at least in part on the digital well plan; compare acquiredinformation associated with drilling of the well with well planinformation of the digital well plan; and output results based at leastin part on the comparing of the acquired information with the well planinformation.

As an example, one or more computer-readable storage media can includecomputer-executable instructions executable to instruct a computingsystem to: receive a digital well plan; issue drilling instructions fordrilling a well based at least in part on the digital well plan; compareacquired information associated with drilling of the well with well planinformation of the digital well plan; and output results based at leastin part on the comparing of the acquired information with the well planinformation.

In some embodiments, a method or methods may be executed by a computingsystem. FIG. 20 shows an example of a system 2000 that can include oneor more computing systems 2001-1, 2001-2, 2001-3 and 2001-4, which maybe operatively coupled via one or more networks 2009, which may includewired and/or wireless networks.

As an example, a system can include an individual computer system or anarrangement of distributed computer systems. In the example of FIG. 20,the computer system 2001-1 can include one or more modules 2002, whichmay be or include processor-executable instructions, for example,executable to perform various tasks (e.g., receiving information,requesting information, processing information, simulation, outputtinginformation, etc.).

As an example, a module may be executed independently, or incoordination with, one or more processors 2004, which is (or are)operatively coupled to one or more storage media 2006 (e.g., via wire,wirelessly, etc.). As an example, one or more of the one or moreprocessors 2004 can be operatively coupled to at least one of one ormore network interface 2007. In such an example, the computer system2001-1 can transmit and/or receive information, for example, via the oneor more networks 2009 (e.g., consider one or more of the Internet, aprivate network, a cellular network, a satellite network, etc.).

As an example, the computer system 2001-1 may receive from and/ortransmit information to one or more other devices, which may be orinclude, for example, one or more of the computer systems 2001-2, etc. Adevice may be located in a physical location that differs from that ofthe computer system 2001-1. As an example, a location may be, forexample, a processing facility location, a data center location (e.g.,server farm, etc.), a rig location, a wellsite location, a downholelocation, etc.

As an example, a processor may be or include a microprocessor,microcontroller, processor module or subsystem, programmable integratedcircuit, programmable gate array, or another control or computingdevice.

As an example, the storage media 2006 may be implemented as one or morecomputer-readable or machine-readable storage media. As an example,storage may be distributed within and/or across multiple internal and/orexternal enclosures of a computing system and/or additional computingsystems.

As an example, a storage medium or storage media may include one or moredifferent forms of memory including semiconductor memory devices such asdynamic or static random access memories (DRAMs or SRAMs), erasable andprogrammable read-only memories (EPROMs), electrically erasable andprogrammable read-only memories (EEPROMs) and flash memories, magneticdisks such as fixed, floppy and removable disks, other magnetic mediaincluding tape, optical media such as compact disks (CDs) or digitalvideo disks (DVDs), BLUERAY® disks, or other types of optical storage,or other types of storage devices.

As an example, a storage medium or media may be located in a machinerunning machine-readable instructions, or located at a remote site fromwhich machine-readable instructions may be downloaded over a network forexecution.

As an example, various components of a system such as, for example, acomputer system, may be implemented in hardware, software, or acombination of both hardware and software (e.g., including firmware),including one or more signal processing and/or application specificintegrated circuits.

As an example, a system may include a processing apparatus that may beor include a general purpose processors or application specific chips(e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriatedevices.

FIG. 21 shows components of a computing system 2100 and a networkedsystem 2110. The system 2100 includes one or more processors 2102,memory and/or storage components 2104, one or more input and/or outputdevices 2106 and a bus 2108. According to an embodiment, instructionsmay be stored in one or more computer-readable media (e.g.,memory/storage components 2104). Such instructions may be read by one ormore processors (e.g., the processor(s) 2102) via a communication bus(e.g., the bus 2108), which may be wired or wireless. The one or moreprocessors may execute such instructions to implement (wholly or inpart) one or more attributes (e.g., as part of a method). A user mayview output from and interact with a process via an I/O device (e.g.,the device 2106). According to an embodiment, a computer-readable mediummay be a storage component such as a physical memory storage device, forexample, a chip, a chip on a package, a memory card, etc.

According to an embodiment, components may be distributed, such as inthe network system 2110. The network system 2110 includes components2122-1, 2122-2, 2122-3, . . . 2122-N. For example, the components 2122-1may include the processor(s) 2102 while the component(s) 2122-3 mayinclude memory accessible by the processor(s) 2102. Further, thecomponent(s) 2122-2 may include an I/O device for display and optionallyinteraction with a method. The network may be or include the Internet,an intranet, a cellular network, a satellite network, etc.

As an example, a device may be a mobile device that includes one or morenetwork interfaces for communication of information. For example, amobile device may include a wireless network interface (e.g., operablevia IEEE 802.11, ETSI GSM, BLUETOOTH®, satellite, etc.). As an example,a mobile device may include components such as a main processor, memory,a display, display graphics circuitry (e.g., optionally including touchand gesture circuitry), a SIM slot, audio/video circuitry, motionprocessing circuitry (e.g., accelerometer, gyroscope), wireless LANcircuitry, smart card circuitry, transmitter circuitry, GPS circuitry,and a battery. As an example, a mobile device may be configured as acell phone, a tablet, etc. As an example, a method may be implemented(e.g., wholly or in part) using a mobile device. As an example, a systemmay include one or more mobile devices.

As an example, a system may be a distributed environment, for example, aso-called “cloud” environment where various devices, components, etc.interact for purposes of data storage, communications, computing, etc.As an example, a device or a system may include one or more componentsfor communication of information via one or more of the Internet (e.g.,where communication occurs via one or more Internet protocols), acellular network, a satellite network, etc. As an example, a method maybe implemented in a distributed environment (e.g., wholly or in part asa cloud-based service).

As an example, information may be input from a display (e.g., consider atouchscreen), output to a display or both. As an example, informationmay be output to a projector, a laser device, a printer, etc. such thatthe information may be viewed. As an example, information may be outputstereographically or holographically. As to a printer, consider a 2D ora 3D printer. As an example, a 3D printer may include one or moresubstances that can be output to construct a 3D object. For example,data may be provided to a 3D printer to construct a 3D representation ofa subterranean formation. As an example, layers may be constructed in 3D(e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example,holes, fractures, etc., may be constructed in 3D (e.g., as positivestructures, as negative structures, etc.).

Although only a few examples have been described in detail above, thoseskilled in the art will readily appreciate that many modifications arepossible in the examples. Accordingly, all such modifications areintended to be included within the scope of this disclosure as definedin the following claims. In the claims, means-plus-function clauses areintended to cover the structures described herein as performing therecited function and not only structural equivalents, but alsoequivalent structures. Thus, although a nail and a screw may not bestructural equivalents in that a nail employs a cylindrical surface tosecure wooden parts together, whereas a screw employs a helical surface,in the environment of fastening wooden parts, a nail and a screw may beequivalent structures. It is the express intention of the applicant notto invoke 35 U.S.C. § 112, paragraph 6 for any limitations of any of theclaims herein, except for those in which the claim expressly uses thewords “means for” together with an associated function.

What is claimed is:
 1. A method (1000) comprising: receiving a digitalwell plan (1010); issuing drilling instructions for drilling a wellbased at least in part on the digital well plan (1020); comparingacquired information associated with drilling of the well with well planinformation of the digital well plan (1030); and outputting resultsbased at least in part on the comparing of the acquired information withthe well plan information (1040).
 2. The method of claim 1 wherein thecomparing comprises determining a difference between the acquiredinformation and the well plan information.
 3. The method of claim 2wherein the comparing comprises inputting the difference to a trainedmachine model that generates results, wherein the results comprise atleast one outcome associated with the difference.
 4. The method of claim2 wherein the comparing comprises performing a search of a databaseutilizing the difference as a query wherein the search generatesresults, wherein the results comprise at least one outcome associatedwith the difference.
 5. The method of claim 2 comprising identifying thedifference as being associated with a positive outcome and/or a negativeoutcome.
 6. The method of claim 2 comprising analyzing the difference todetermine at least one factor of the digital well plan as being at leastin part an underlying cause of the difference.
 7. The method of claim 2wherein the difference comprises a difference in a rate of penetrationof the drilling or a difference in a dogleg of a wellbore of the well.8. The method of claim 2 comprising analyzing the difference to identifyat least one factor specified in the digital well plan as beingassociated with the difference, optionally wherein the at least onefactor comprises at least one of an equipment factor, an operationalfactor and a formation factor.
 9. The method of claim 1 wherein theoutputting comprises storing the results to a database.
 10. The methodof claim 1 comprising generating the digital well plan.
 11. The methodof claim 10 wherein the generating the digital well plan comprisesreceiving information for the well, selecting one or more offset wells,accessing comparison results for the one or more offset wells, andgenerating the digital well plan based at least in part on at least aportion of the comparison results for the one or more offset wells. 12.The method of claim 1 comprising revising the digital well plan, whereinthe revising comprises revising the digital well plan based at least inpart on at least a portion of the results.
 13. The method of claim 1wherein the digital well plan comprises at least one member selectedfrom a group consisting of a trajectory factor, a bottom hole assemblyfactor, and an operational factor.
 14. A system (770) comprising: one ormore processors (772); memory (774) operatively coupled to the one ormore processors; and processor-executable instructions (776) stored inthe memory and executable by at least one of the one or more processorsto instruct the system to: receive a digital well plan (1011); issuedrilling instructions for drilling a well based at least in part on thedigital well plan (1021); compare acquired information associated withdrilling of the well with well plan information of the digital well plan(1031); and output results based at least in part on the comparing ofthe acquired information with the well plan information (1041).
 15. Oneor more computer-readable storage media comprising computer-executableinstructions executable to instruct a computing system to perform amethod according to any of claims 1 to 13.